Find predictive signals in history

Use NixtlaClient.explain() when someone asks:

Which signals in my historical data deserve attention?

The result ranks your features using their past relationship with the target. You do not need to make a forecast or provide future feature values.

The analysis runs as an asynchronous job on the server. NixtlaClient.explain() submits the job and polls its status until the weights are ready, so the call blocks like any other client method. By default the client waits up to 10 minutes; adjust async_job_wait_timeout and async_job_poll_interval when creating the NixtlaClient if you need a different behavior. A job that fails on the server raises nixtla.AsyncJobError with the server’s original error. Server-side cancellation raises nixtla.AsyncJobCancelledError; exceeding the client wait timeout raises nixtla.AsyncJobTimeoutError and requests cancellation.

These weights are not causal. They measure whether a feature’s past values help predict the target, which is a statement about correlation over time, not about cause and effect. A high weight does not mean that changing the feature will change the target: both may be driven by something absent from your data, or the direction of influence may run the other way. To ask what a forecast is sensitive to, use intervention analysis instead.

Retail-demand example

The store in our example has one year of daily demand, price, promotion, and temperature data.

Create the example data

import numpy as np
import pandas as pd

from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # Defaults to os.environ["NIXTLA_API_KEY"]
    api_key="my_api_key_provided_by_nixtla"
)

rng = np.random.default_rng(7)
n = 365
h = 14
t = np.arange(n + h)

price = 20 + 1.8 * np.sin(2 * np.pi * t / 90) + rng.normal(0, 0.35, n + h)
promotion = ((t % 42) >= 35).astype(int)
temperature = (
    18
    + 9 * np.sin(2 * np.pi * (t - 30) / 365)
    + rng.normal(0, 0.8, n + h)
)
weekly = 6 * np.sin(2 * np.pi * t / 7)
demand = (
    118
    - 2.4 * price[:n]
    + 16 * promotion[:n]
    + 0.55 * temperature[:n]
    + weekly[:n]
    + rng.normal(0, 2.0, n)
)

df = pd.DataFrame(
    {
        "unique_id": "store-a",
        "ds": pd.date_range("2024-01-01", periods=n, freq="D"),
        "y": demand,
        "price": price[:n],
        "promotion": promotion[:n],
        "temperature": temperature[:n],
    }
)

Rank the historical signals

Call explain() with the features you want to review:

signals = nixtla_client.explain(
    df=df,
    features=["price", "promotion", "temperature"],
)

signals

feature	weight	method
price	0.000	granger
promotion	0.863	granger
temperature	0.137	granger

The weights add up to one. In this historical window, promotion has the strongest measured signal, followed by temperature. Price does not add a measurable linear lagged signal in this particular run.

A horizontal bar chart ranking promotion, temperature, and price as historical predictive signals — Promotion is the strongest historical signal in the default analysis. Results were generated with TimeGPT 2.1.

Use the ranking

This result can help you:

Decide which data sources deserve closer monitoring.
Prioritize features for a forecasting experiment.
Check whether important business signals are present in the data.
Compare feature rankings across stores, products, or time periods.

It cannot tell you what to change. “Promotion ranks first, so running more promotions will raise demand” does not follow from this ranking; it is a causal claim the analysis does not support. Weights are relative scores. A weight of 0.863 means promotion is the strongest signal among these three features; it is not a percentage of the forecast.

Use your own data

By default, explain() analyzes every column except the series ID, timestamp, and target. Passing features makes the scope explicit:

signals = nixtla_client.explain(
    df=df,
    features=["price", "promotion", "temperature"],
)

For multiple time series, stack the series in the same dataframe and identify them with unique_id. TimeGPT respects the boundary between each series and returns one combined ranking. Non-numeric features must be declared as categorical. Any feature that is not numeric and not listed in categorical_exog_list is rejected, naming the column:

df_with_campaign = df.assign(
    campaign=np.where(df["promotion"] == 1, "email", "none")
)

signals = nixtla_client.explain(
    df=df_with_campaign,
    features=["price", "promotion", "temperature", "campaign"],
    categorical_exog_list=["campaign"],
)

Both methods are lag-based, so every series must be complete and regularly spaced. explain() infers the frequency from df; pass freq explicitly for polars input, or to be strict about which spacing you expect:

signals = nixtla_client.explain(df=df, freq="D")

Historical signals are especially useful for exploration and feature prioritization. Re-run an important analysis on another time period to see whether the same signals remain useful.

INTRODUCTION

SETUP

DATA REQUIREMENTS

FORECASTING

ANOMALY DETECTION

USE CASES

REFERENCE

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Retail-demand example

Rank the historical signals

Use the ranking

Use your own data

Next

​Retail-demand example

​Rank the historical signals

​Use the ranking

​Use your own data

​Next

Retail-demand example

Rank the historical signals

Use the ranking

Use your own data

Next