{
  "openapi": "3.1.0",
  "info": {
    "title": "Nixtla Forecast API",
    "description": "API for TimeGPT forecast. Just send your data as json and get results. We do the heavy lifting.",
    "version": "0.2.8"
  },
  "paths": {
    "/v2/forecast": {
      "post": {
        "summary": "Foundational Time Series Model Multi Series",
        "description": "Based on the provided data, this endpoint predicts the future values of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the predicted values for each series based on the input arguments. Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
        "operationId": "v2_forecast_v2_forecast_post",
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/ForecastInput",
                "examples": [
                  {
                    "freq": "D",
                    "h": 2,
                    "series": {
                      "sizes": [
                        5,
                        3
                      ],
                      "y": [
                        1,
                        2,
                        3,
                        4,
                        5,
                        10,
                        20,
                        30
                      ]
                    }
                  }
                ]
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/ForecastOutput"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ],
        "x-fern-sdk-method-name": "v2/forecast"
      }
    },
    "/v2/anomaly_detection": {
      "post": {
        "summary": "Foundational Time Series Model Multi Series Anomaly Detector",
        "description": "Based on the provided data, this endpoint detects the anomalies in the historical perdiod of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
        "operationId": "v2_anomaly_detection_v2_anomaly_detection_post",
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/AnomalyDetectionInput",
                "examples": [
                  {
                    "freq": "D",
                    "level": 90,
                    "series": {
                      "sizes": [
                        35
                      ],
                      "y": [
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                        6,
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                        6,
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                        6,
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                        6,
                        0,
                        1,
                        2,
                        10,
                        4,
                        5,
                        6
                      ]
                    }
                  }
                ]
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/AnomalyDetectionOutput"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ],
        "x-fern-sdk-method-name": "v2/anomaly_detection"
      }
    },
    "/v2/online_anomaly_detection": {
      "post": {
        "summary": "Foundational Time Series Model Online Multi Series Anomaly Detector",
        "description": "This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
        "operationId": "v2_online_anomaly_detection_v2_online_anomaly_detection_post",
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/OnlineAnomalyInput",
                "examples": [
                  {
                    "detection_size": 5,
                    "freq": "W",
                    "h": 20,
                    "level": 99,
                    "series": {
                      "sizes": [
                        320
                      ],
                      "y": [
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                        30,
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                      ]
                    }
                  }
                ]
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/OnlineAnomalyOutput"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ],
        "x-fern-sdk-method-name": "v2/online_anomaly_detection"
      }
    },
    "/model_params": {
      "get": {
        "tags": [
          "excluded"
        ],
        "summary": "Get Model Params",
        "operationId": "get_model_params_model_params_get",
        "security": [
          {
            "HTTPBearer": []
          }
        ],
        "parameters": [
          {
            "name": "model",
            "in": "query",
            "required": true,
            "schema": {
              "type": "string",
              "title": "Model"
            }
          },
          {
            "name": "freq",
            "in": "query",
            "required": true,
            "schema": {
              "type": "string",
              "title": "Freq"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {}
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "x-excluded": true
      }
    },
    "/v2/cross_validation": {
      "post": {
        "summary": "Foundational Time Series Model Multi Series Cross Validation",
        "description": "Perform Cross Validation for multiple series",
        "operationId": "v2_cross_validation_v2_cross_validation_post",
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/CrossValidationInput",
                "examples": [
                  {
                    "freq": "D",
                    "h": 2,
                    "n_windows": 1,
                    "series": {
                      "sizes": [
                        5,
                        3
                      ],
                      "y": [
                        1,
                        2,
                        3,
                        4,
                        5,
                        10,
                        20,
                        30
                      ]
                    }
                  }
                ]
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/CrossValidationOutput"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ],
        "x-fern-sdk-method-name": "v2/cross_validation"
      }
    },
    "/v2/finetune": {
      "post": {
        "summary": "Foundational Time Series Model Multi Series Finetuning",
        "description": "Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.",
        "operationId": "v2_finetune_v2_finetune_post",
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/FinetuneInput",
                "examples": [
                  {
                    "finetune_steps": 10,
                    "freq": "MS",
                    "model": "timegpt-1",
                    "series": {
                      "sizes": [
                        36
                      ],
                      "y": [
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                        6,
                        7,
                        8,
                        9,
                        10,
                        11,
                        12,
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                        14,
                        15,
                        16,
                        17,
                        18,
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                        20,
                        21,
                        22,
                        23,
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                        25,
                        26,
                        27,
                        28,
                        29,
                        30,
                        31,
                        32,
                        33,
                        34,
                        35
                      ]
                    }
                  }
                ]
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/FinetuneOutput"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ],
        "x-fern-sdk-method-name": "v2/finetune"
      }
    },
    "/v2/finetuned_models": {
      "get": {
        "summary": "List Fine-tuned Models",
        "description": "List all the finetuned models that you have created. The response contains a list with the IDs of the models that you have fine-tuned and are available to make forecasts.",
        "operationId": "v2_finetuned_models_v2_finetuned_models_get",
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/FinetunedModelsOutput"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ]
      }
    },
    "/v2/finetuned_models/{finetuned_model_id}": {
      "get": {
        "summary": "Get single Fine-tuned Model",
        "description": "Retrieve metadata for a previously fine-tuned model. The response contains the metadata of a model that you have fine-tuned and is available to make forecasts.",
        "operationId": "v2_finetuned_model_v2_finetuned_models__finetuned_model_id__get",
        "parameters": [
          {
            "name": "finetuned_model_id",
            "in": "path",
            "required": true,
            "schema": {
              "type": "string",
              "pattern": "^[a-zA-Z0-9\\-_]{1,36}$",
              "title": "Finetuned Model Id"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/FinetunedModel"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ]
      },
      "delete": {
        "summary": "Delete Fine-tuned Model",
        "description": "Delete a previously saved finetuned model. It takes the ID of the model that you want to delete as a path parameter.",
        "operationId": "v2_finetuned_models_delete_v2_finetuned_models__finetuned_model_id__delete",
        "parameters": [
          {
            "name": "finetuned_model_id",
            "in": "path",
            "required": true,
            "schema": {
              "type": "string",
              "pattern": "^[a-zA-Z0-9\\-_]{1,36}$",
              "title": "Finetuned Model Id"
            }
          }
        ],
        "responses": {
          "204": {
            "description": "Successful Response"
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/HTTPValidationError"
                }
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ]
      }
    },
    "/validate_api_key": {
      "get": {
        "summary": "Validate Api Key",
        "operationId": "validate_api_key_validate_api_key_get",
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {}
              }
            }
          }
        },
        "security": [
          {
            "HTTPBearer": []
          }
        ]
      }
    }
  },
  "components": {
    "schemas": {
      "AnomalyDetectionInput": {
        "properties": {
          "series": {
            "$ref": "#/components/schemas/SeriesWithFutureExogenous"
          },
          "freq": {
            "type": "string",
            "title": "Freq",
            "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
          },
          "model": {
            "type": "string",
            "title": "Model",
            "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.",
            "default": "timegpt-1"
          },
          "clean_ex_first": {
            "type": "boolean",
            "title": "Clean Ex First",
            "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.",
            "default": true
          },
          "finetuned_model_id": {
            "anyOf": [
              {
                "type": "string",
                "pattern": "^[a-zA-Z0-9\\-_]{1,36}$"
              },
              {
                "type": "null"
              }
            ],
            "title": "Finetuned Model Id",
            "description": "ID of previously finetuned model"
          },
          "multivariate": {
            "type": "boolean",
            "title": "Multivariate",
            "description": "Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.",
            "default": false
          },
          "model_parameters": {
            "anyOf": [
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Model Parameters",
            "description": "Optional dictionary of parameters to customize the behavior of the large time model. "
          },
          "hist_exog": {
            "anyOf": [
              {
                "items": {
                  "type": "integer",
                  "minimum": 0
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Hist Exog",
            "description": "Zero-based indices of the exogenous features to treat as historical."
          },
          "level": {
            "anyOf": [
              {
                "type": "integer",
                "exclusiveMaximum": 100,
                "minimum": 0
              },
              {
                "type": "number",
                "exclusiveMaximum": 100,
                "minimum": 0
              }
            ],
            "title": "Level",
            "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.",
            "default": 99
          },
          "feature_contributions": {
            "type": "boolean",
            "title": "Feature Contributions",
            "description": "Compute the exogenous features contributions to the forecast.",
            "default": false
          }
        },
        "type": "object",
        "required": [
          "series",
          "freq"
        ],
        "title": "AnomalyDetectionInput"
      },
      "AnomalyDetectionOutput": {
        "properties": {
          "input_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Input Tokens"
          },
          "output_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Output Tokens"
          },
          "finetune_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Tokens"
          },
          "mean": {
            "items": {
              "type": "number"
            },
            "type": "array",
            "title": "Mean"
          },
          "sizes": {
            "items": {
              "type": "integer"
            },
            "type": "array",
            "title": "Sizes"
          },
          "intervals": {
            "anyOf": [
              {
                "additionalProperties": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Intervals"
          },
          "weights_x": {
            "anyOf": [
              {
                "items": {
                  "type": "number"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Weights X"
          },
          "feature_contributions": {
            "anyOf": [
              {
                "items": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Feature Contributions"
          },
          "anomaly": {
            "items": {
              "type": "boolean"
            },
            "type": "array",
            "title": "Anomaly"
          }
        },
        "type": "object",
        "required": [
          "input_tokens",
          "output_tokens",
          "finetune_tokens",
          "mean",
          "sizes",
          "anomaly"
        ],
        "title": "AnomalyDetectionOutput"
      },
      "CrossValidationInput": {
        "properties": {
          "series": {
            "$ref": "#/components/schemas/SeriesWithFutureExogenous"
          },
          "freq": {
            "type": "string",
            "title": "Freq",
            "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
          },
          "n_windows": {
            "type": "integer",
            "exclusiveMinimum": 0,
            "title": "N Windows",
            "description": "Number of windows to evaluate.",
            "default": 1
          },
          "h": {
            "type": "integer",
            "exclusiveMinimum": 0,
            "title": "H",
            "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict."
          },
          "full_history": {
            "type": "boolean",
            "title": "Full History",
            "description": "Forecast across the entire series history (the `add_history` use case). The horizon and number of windows are derived server-side (any supplied `h` / `n_windows` are ignored), and the exogenous model is refit a bounded number of times to keep whole-history requests fast. Has no effect without exogenous features.",
            "default": false
          },
          "model": {
            "type": "string",
            "title": "Model",
            "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.",
            "default": "timegpt-1"
          },
          "clean_ex_first": {
            "type": "boolean",
            "title": "Clean Ex First",
            "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.",
            "default": true
          },
          "level": {
            "anyOf": [
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "integer",
                      "exclusiveMaximum": 100,
                      "minimum": 0
                    },
                    {
                      "type": "number",
                      "exclusiveMaximum": 100,
                      "minimum": 0
                    }
                  ]
                },
                "type": "array",
                "minItems": 1
              },
              {
                "type": "null"
              }
            ],
            "title": "Level",
            "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals."
          },
          "finetune_steps": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Steps",
            "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.",
            "default": 0
          },
          "finetune_loss": {
            "type": "string",
            "enum": [
              "default",
              "mae",
              "mse",
              "rmse",
              "mape",
              "smape",
              "poisson"
            ],
            "title": "Finetune Loss",
            "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.",
            "default": "default"
          },
          "finetune_depth": {
            "type": "integer",
            "enum": [
              1,
              2,
              3,
              4,
              5
            ],
            "title": "Finetune Depth",
            "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models, meanwhile it has no effect on the other models. By default, the value is set to 1.",
            "default": 1
          },
          "finetuned_model_id": {
            "anyOf": [
              {
                "type": "string",
                "pattern": "^[a-zA-Z0-9\\-_]{1,36}$"
              },
              {
                "type": "null"
              }
            ],
            "title": "Finetuned Model Id",
            "description": "ID of previously finetuned model"
          },
          "step_size": {
            "anyOf": [
              {
                "type": "integer",
                "exclusiveMinimum": 0
              },
              {
                "type": "null"
              }
            ],
            "title": "Step Size",
            "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon."
          },
          "hist_exog": {
            "anyOf": [
              {
                "items": {
                  "type": "integer",
                  "minimum": 0
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Hist Exog",
            "description": "Zero-based indices of the exogenous features to treat as historical."
          },
          "refit": {
            "type": "boolean",
            "title": "Refit",
            "description": "Fine-tune the model in each window. If `False`, only fine-tunes on the first window. Only used if `finetune_steps` > 0.",
            "default": true
          },
          "multivariate": {
            "type": "boolean",
            "title": "Multivariate",
            "description": "Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.",
            "default": false
          },
          "model_parameters": {
            "anyOf": [
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Model Parameters",
            "description": "Optional dictionary of parameters to customize the behavior of the large time model. "
          },
          "feature_contributions": {
            "type": "boolean",
            "title": "Feature Contributions",
            "description": "Compute the exogenous features contributions to the forecast.",
            "default": false
          }
        },
        "type": "object",
        "required": [
          "series",
          "freq",
          "h"
        ],
        "title": "CrossValidationInput"
      },
      "CrossValidationOutput": {
        "properties": {
          "input_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Input Tokens"
          },
          "output_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Output Tokens"
          },
          "finetune_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Tokens"
          },
          "mean": {
            "items": {
              "type": "number"
            },
            "type": "array",
            "title": "Mean"
          },
          "sizes": {
            "items": {
              "type": "integer"
            },
            "type": "array",
            "title": "Sizes"
          },
          "idxs": {
            "items": {
              "type": "integer"
            },
            "type": "array",
            "title": "Idxs"
          },
          "intervals": {
            "anyOf": [
              {
                "additionalProperties": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Intervals"
          },
          "feature_contributions": {
            "anyOf": [
              {
                "items": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Feature Contributions"
          }
        },
        "type": "object",
        "required": [
          "input_tokens",
          "output_tokens",
          "finetune_tokens",
          "mean",
          "sizes",
          "idxs"
        ],
        "title": "CrossValidationOutput"
      },
      "FinetuneInput": {
        "properties": {
          "series": {
            "$ref": "#/components/schemas/SeriesWithFutureExogenous"
          },
          "freq": {
            "type": "string",
            "title": "Freq",
            "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
          },
          "model": {
            "type": "string",
            "title": "Model",
            "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.",
            "default": "timegpt-1"
          },
          "finetune_steps": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Steps",
            "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.",
            "default": 10
          },
          "finetune_loss": {
            "type": "string",
            "enum": [
              "default",
              "mae",
              "mse",
              "rmse",
              "mape",
              "smape",
              "poisson"
            ],
            "title": "Finetune Loss",
            "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.",
            "default": "default"
          },
          "finetune_depth": {
            "type": "integer",
            "enum": [
              1,
              2,
              3,
              4,
              5
            ],
            "title": "Finetune Depth",
            "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models, meanwhile it has no effect on the other models. By default, the value is set to 1.",
            "default": 1
          },
          "output_model_id": {
            "anyOf": [
              {
                "type": "string",
                "pattern": "^[a-zA-Z0-9\\-_]{1,36}$"
              },
              {
                "type": "null"
              }
            ],
            "title": "Output Model Id",
            "description": "ID to assign to the finetuned model"
          },
          "finetuned_model_id": {
            "anyOf": [
              {
                "type": "string",
                "pattern": "^[a-zA-Z0-9\\-_]{1,36}$"
              },
              {
                "type": "null"
              }
            ],
            "title": "Finetuned Model Id",
            "description": "ID of previously finetuned model"
          },
          "hist_exog": {
            "anyOf": [
              {
                "items": {
                  "type": "integer",
                  "minimum": 0
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Hist Exog",
            "description": "Zero-based indices of the exogenous features to treat as historical."
          },
          "multivariate": {
            "type": "boolean",
            "title": "Multivariate",
            "description": "Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.",
            "default": false
          },
          "model_parameters": {
            "anyOf": [
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Model Parameters",
            "description": "Optional dictionary of parameters to customize the behavior of the large time model. "
          }
        },
        "type": "object",
        "required": [
          "series",
          "freq"
        ],
        "title": "FinetuneInput"
      },
      "FinetuneOutput": {
        "properties": {
          "input_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Input Tokens"
          },
          "output_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Output Tokens"
          },
          "finetune_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Tokens"
          },
          "finetuned_model_id": {
            "type": "string",
            "pattern": "^[a-zA-Z0-9\\-_]{1,36}$",
            "title": "Finetuned Model Id"
          }
        },
        "type": "object",
        "required": [
          "input_tokens",
          "output_tokens",
          "finetune_tokens",
          "finetuned_model_id"
        ],
        "title": "FinetuneOutput"
      },
      "FinetunedModel": {
        "properties": {
          "id": {
            "type": "string",
            "pattern": "^[a-zA-Z0-9\\-_]{1,36}$",
            "title": "Id"
          },
          "created_at": {
            "type": "string",
            "title": "Created At"
          },
          "created_by": {
            "type": "string",
            "title": "Created By",
            "default": "user"
          },
          "base_model_id": {
            "type": "string",
            "pattern": "^[a-zA-Z0-9\\-_]{1,36}$",
            "title": "Base Model Id"
          },
          "steps": {
            "type": "integer",
            "title": "Steps"
          },
          "depth": {
            "type": "integer",
            "title": "Depth"
          },
          "loss": {
            "type": "string",
            "enum": [
              "default",
              "mae",
              "mse",
              "rmse",
              "mape",
              "smape",
              "poisson"
            ],
            "title": "Loss"
          },
          "model": {
            "type": "string",
            "title": "Model"
          },
          "freq": {
            "type": "string",
            "title": "Freq"
          }
        },
        "type": "object",
        "required": [
          "id",
          "created_at",
          "base_model_id",
          "steps",
          "depth",
          "loss",
          "model",
          "freq"
        ],
        "title": "FinetunedModel"
      },
      "FinetunedModelsOutput": {
        "properties": {
          "finetuned_models": {
            "items": {
              "$ref": "#/components/schemas/FinetunedModel"
            },
            "type": "array",
            "title": "Finetuned Models"
          }
        },
        "type": "object",
        "required": [
          "finetuned_models"
        ],
        "title": "FinetunedModelsOutput"
      },
      "ForecastInput": {
        "properties": {
          "series": {
            "$ref": "#/components/schemas/SeriesWithFutureExogenous"
          },
          "freq": {
            "type": "string",
            "title": "Freq",
            "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
          },
          "h": {
            "type": "integer",
            "exclusiveMinimum": 0,
            "title": "H",
            "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict."
          },
          "model": {
            "type": "string",
            "title": "Model",
            "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.",
            "default": "timegpt-1"
          },
          "clean_ex_first": {
            "type": "boolean",
            "title": "Clean Ex First",
            "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.",
            "default": true
          },
          "level": {
            "anyOf": [
              {
                "items": {
                  "anyOf": [
                    {
                      "type": "integer",
                      "exclusiveMaximum": 100,
                      "minimum": 0
                    },
                    {
                      "type": "number",
                      "exclusiveMaximum": 100,
                      "minimum": 0
                    }
                  ]
                },
                "type": "array",
                "minItems": 1
              },
              {
                "type": "null"
              }
            ],
            "title": "Level",
            "description": "A list of values representing the prediction intervals. Each value is a percentage that indicates the level of certainty for the corresponding prediction interval. For example, [80, 90] defines 80% and 90% prediction intervals."
          },
          "finetune_steps": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Steps",
            "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.",
            "default": 0
          },
          "finetune_loss": {
            "type": "string",
            "enum": [
              "default",
              "mae",
              "mse",
              "rmse",
              "mape",
              "smape",
              "poisson"
            ],
            "title": "Finetune Loss",
            "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.",
            "default": "default"
          },
          "finetune_depth": {
            "type": "integer",
            "enum": [
              1,
              2,
              3,
              4,
              5
            ],
            "title": "Finetune Depth",
            "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models, meanwhile it has no effect on the other models. By default, the value is set to 1.",
            "default": 1
          },
          "finetuned_model_id": {
            "anyOf": [
              {
                "type": "string",
                "pattern": "^[a-zA-Z0-9\\-_]{1,36}$"
              },
              {
                "type": "null"
              }
            ],
            "title": "Finetuned Model Id",
            "description": "ID of previously finetuned model"
          },
          "feature_contributions": {
            "type": "boolean",
            "title": "Feature Contributions",
            "description": "Compute the exogenous features contributions to the forecast.",
            "default": false
          },
          "multivariate": {
            "type": "boolean",
            "title": "Multivariate",
            "description": "Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.",
            "default": false
          },
          "model_parameters": {
            "anyOf": [
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Model Parameters",
            "description": "Optional dictionary of parameters to customize the behavior of the large time model. "
          }
        },
        "type": "object",
        "required": [
          "series",
          "freq",
          "h"
        ],
        "title": "ForecastInput"
      },
      "ForecastOutput": {
        "properties": {
          "input_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Input Tokens"
          },
          "output_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Output Tokens"
          },
          "finetune_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Tokens"
          },
          "mean": {
            "items": {
              "type": "number"
            },
            "type": "array",
            "title": "Mean"
          },
          "intervals": {
            "anyOf": [
              {
                "additionalProperties": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Intervals"
          },
          "weights_x": {
            "anyOf": [
              {
                "items": {
                  "type": "number"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Weights X"
          },
          "feature_contributions": {
            "anyOf": [
              {
                "items": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Feature Contributions"
          }
        },
        "type": "object",
        "required": [
          "input_tokens",
          "output_tokens",
          "finetune_tokens",
          "mean"
        ],
        "title": "ForecastOutput"
      },
      "HTTPValidationError": {
        "properties": {
          "detail": {
            "items": {
              "$ref": "#/components/schemas/ValidationError"
            },
            "type": "array",
            "title": "Detail"
          }
        },
        "type": "object",
        "title": "HTTPValidationError"
      },
      "OnlineAnomalyInput": {
        "properties": {
          "series": {
            "$ref": "#/components/schemas/SeriesWithFutureExogenous"
          },
          "freq": {
            "type": "string",
            "title": "Freq",
            "description": "The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available."
          },
          "detection_size": {
            "type": "integer",
            "exclusiveMinimum": 0,
            "title": "Detection Size",
            "description": "Window over which to detect anomalies starting from the end of the series. This window is not considered when calculating the anomaly threshold to avoid bias from abnormal samples, unless there are less than 6 * detection_size forecasted samples."
          },
          "threshold_method": {
            "type": "string",
            "enum": [
              "univariate",
              "multivariate"
            ],
            "title": "Threshold Method",
            "description": "The thresholding method to detect anomalies",
            "default": "univariate"
          },
          "h": {
            "type": "integer",
            "exclusiveMinimum": 0,
            "title": "H",
            "description": "The forecasting horizon. This represents the number of time steps into the future that the forecast should predict."
          },
          "model": {
            "type": "string",
            "title": "Model",
            "description": "Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data.",
            "default": "timegpt-1"
          },
          "clean_ex_first": {
            "type": "boolean",
            "title": "Clean Ex First",
            "description": "A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.",
            "default": true
          },
          "level": {
            "anyOf": [
              {
                "type": "integer",
                "exclusiveMaximum": 100,
                "minimum": 0
              },
              {
                "type": "number",
                "exclusiveMaximum": 100,
                "minimum": 0
              }
            ],
            "title": "Level",
            "description": "Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.",
            "default": 99
          },
          "finetune_steps": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Steps",
            "description": "The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.",
            "default": 0
          },
          "finetune_loss": {
            "type": "string",
            "enum": [
              "default",
              "mae",
              "mse",
              "rmse",
              "mape",
              "smape",
              "poisson"
            ],
            "title": "Finetune Loss",
            "description": "The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.",
            "default": "default"
          },
          "finetune_depth": {
            "type": "integer",
            "enum": [
              1,
              2,
              3,
              4,
              5
            ],
            "title": "Finetune Depth",
            "description": "The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models, meanwhile it has no effect on the other models. By default, the value is set to 1.",
            "default": 1
          },
          "finetuned_model_id": {
            "anyOf": [
              {
                "type": "string",
                "pattern": "^[a-zA-Z0-9\\-_]{1,36}$"
              },
              {
                "type": "null"
              }
            ],
            "title": "Finetuned Model Id",
            "description": "ID of previously finetuned model"
          },
          "step_size": {
            "anyOf": [
              {
                "type": "integer",
                "exclusiveMinimum": 0
              },
              {
                "type": "null"
              }
            ],
            "title": "Step Size",
            "description": "Step size between each cross validation window. If None it will be equal to the forecasting horizon."
          },
          "hist_exog": {
            "anyOf": [
              {
                "items": {
                  "type": "integer",
                  "minimum": 0
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Hist Exog",
            "description": "Zero-based indices of the exogenous features to treat as historical."
          },
          "refit": {
            "type": "boolean",
            "title": "Refit",
            "description": "Fine-tune the model in each window. If `False`, only fine-tunes on the first window. Only used if `finetune_steps` > 0.",
            "default": true
          },
          "multivariate": {
            "type": "boolean",
            "title": "Multivariate",
            "description": "Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.",
            "default": false
          },
          "model_parameters": {
            "anyOf": [
              {
                "additionalProperties": true,
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Model Parameters",
            "description": "Optional dictionary of parameters to customize the behavior of the large time model. "
          },
          "feature_contributions": {
            "type": "boolean",
            "title": "Feature Contributions",
            "description": "Compute the exogenous features contributions to the forecast.",
            "default": false
          }
        },
        "type": "object",
        "required": [
          "series",
          "freq",
          "detection_size",
          "h"
        ],
        "title": "OnlineAnomalyInput"
      },
      "OnlineAnomalyOutput": {
        "properties": {
          "input_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Input Tokens"
          },
          "output_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Output Tokens"
          },
          "finetune_tokens": {
            "type": "integer",
            "minimum": 0,
            "title": "Finetune Tokens"
          },
          "mean": {
            "items": {
              "type": "number"
            },
            "type": "array",
            "title": "Mean"
          },
          "sizes": {
            "items": {
              "type": "integer"
            },
            "type": "array",
            "title": "Sizes"
          },
          "idxs": {
            "items": {
              "type": "integer"
            },
            "type": "array",
            "title": "Idxs"
          },
          "anomaly": {
            "items": {
              "type": "boolean"
            },
            "type": "array",
            "title": "Anomaly"
          },
          "anomaly_score": {
            "items": {
              "type": "number"
            },
            "type": "array",
            "title": "Anomaly Score"
          },
          "accumulated_anomaly_score": {
            "anyOf": [
              {
                "items": {
                  "type": "number"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Accumulated Anomaly Score"
          },
          "intervals": {
            "anyOf": [
              {
                "additionalProperties": {
                  "items": {
                    "type": "number"
                  },
                  "type": "array"
                },
                "type": "object"
              },
              {
                "type": "null"
              }
            ],
            "title": "Intervals"
          }
        },
        "type": "object",
        "required": [
          "input_tokens",
          "output_tokens",
          "finetune_tokens",
          "mean",
          "sizes",
          "idxs",
          "anomaly",
          "anomaly_score"
        ],
        "title": "OnlineAnomalyOutput"
      },
      "SeriesWithFutureExogenous": {
        "properties": {
          "X_future": {
            "anyOf": [
              {
                "items": {
                  "items": {
                    "anyOf": [
                      {
                        "type": "number"
                      },
                      {
                        "type": "string"
                      }
                    ]
                  },
                  "type": "array"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "X Future",
            "description": "Future values of the exogenous features. Each feature must be a list of size number of series times the forecast horizon (h)."
          },
          "X": {
            "anyOf": [
              {
                "items": {
                  "items": {
                    "anyOf": [
                      {
                        "type": "number"
                      },
                      {
                        "type": "string"
                      }
                    ]
                  },
                  "type": "array"
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "X",
            "description": "Historic values of the exogenous features. Each feature must be a list of the same size as the target (y)."
          },
          "categorical_exog": {
            "anyOf": [
              {
                "items": {
                  "type": "integer",
                  "minimum": 0
                },
                "type": "array"
              },
              {
                "type": "null"
              }
            ],
            "title": "Categorical Exog",
            "description": "Zero-based indices of the columns in X that are categorical features."
          },
          "y": {
            "items": {
              "type": "number"
            },
            "type": "array",
            "title": "Y",
            "description": "Historic values of the target."
          },
          "sizes": {
            "items": {
              "type": "integer"
            },
            "type": "array",
            "title": "Sizes",
            "description": "Sizes of the individual series."
          }
        },
        "type": "object",
        "required": [
          "y",
          "sizes"
        ],
        "title": "SeriesWithFutureExogenous"
      },
      "ValidationError": {
        "properties": {
          "loc": {
            "items": {
              "anyOf": [
                {
                  "type": "string"
                },
                {
                  "type": "integer"
                }
              ]
            },
            "type": "array",
            "title": "Location"
          },
          "msg": {
            "type": "string",
            "title": "Message"
          },
          "type": {
            "type": "string",
            "title": "Error Type"
          },
          "input": {
            "title": "Input"
          },
          "ctx": {
            "type": "object",
            "title": "Context"
          }
        },
        "type": "object",
        "required": [
          "loc",
          "msg",
          "type"
        ],
        "title": "ValidationError"
      }
    },
    "securitySchemes": {
      "HTTPBearer": {
        "type": "http",
        "description": "HTTPBearer",
        "scheme": "bearer"
      }
    }
  },
  "servers": [
    {
      "url": "https://api.nixtla.io"
    }
  ]
}