Multiple series forecasting

TimeGPT provides a robust solution for multi-series forecasting, which involves analyzing multiple data series concurrently, rather than a single one. The tool can be fine-tuned using a broad collection of series, enabling you to tailor the model to suit your specific needs or tasks.

Note that the forecasts are still univariate. This means that although TimeGPT is a global model, it won’t consider the inter-feature relationships within the target series. However, TimeGPT does support the use of exogenous variables such as categorical variables (e.g., category, brand), numerical variables (e.g., temperature, prices), or even special holidays.

Let’s see this in action.

1. Import packages

First, we install and import the required packages and initialize the Nixtla client.

As always, we start off by intializing an instance of NixtlaClient.

import pandas as pd
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)

👍 Use an Azure AI endpoint

To use an Azure AI endpoint, remember to set also the base_url argument:

nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")

2. Load the data

The following dataset contains prices of different electricity markets in Europe.

Mutliple series are automatically detected in TimeGPT using the unique_id column. This column contains labels for each series. If there are multiple unique values in that column, then it knows it is handling a multi-series scneario.

In this particular case, the unique_id column contains the value BE, DE, FR, JPM, and NP.

df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv')
df.head()

	unique_id	ds	y
0	BE	2016-12-01 00:00:00	72.00
1	BE	2016-12-01 01:00:00	65.80
2	BE	2016-12-01 02:00:00	59.99
3	BE	2016-12-01 03:00:00	50.69
4	BE	2016-12-01 04:00:00	52.58

Let’s plot this series using [NixtlaClient](src/nixtla_client.html#nixtlaclient):

nixtla_client.plot(df)

3. Forecasting Multiple Series

To forecast all series at once, we simply pass the dataframe to the df argument. TimeGPt will automatically forecast all series.

timegpt_fcst_multiseries_df = nixtla_client.forecast(df=df, h=24, level=[80, 90])
timegpt_fcst_multiseries_df.head()

INFO:nixtlats.nixtla_client:Validating inputs...
INFO:nixtlats.nixtla_client:Preprocessing dataframes...
INFO:nixtlats.nixtla_client:Inferred freq: H
INFO:nixtlats.nixtla_client:Restricting input...
INFO:nixtlats.nixtla_client:Calling Forecast Endpoint...

	unique_id	ds	TimeGPT	TimeGPT-lo-90	TimeGPT-lo-80	TimeGPT-hi-80	TimeGPT-hi-90
0	BE	2016-12-31 00:00:00	46.151176	36.660478	38.337019	53.965334	55.641875
1	BE	2016-12-31 01:00:00	42.426598	31.602231	33.976724	50.876471	53.250964
2	BE	2016-12-31 02:00:00	40.242889	30.439970	33.634985	46.850794	50.045809
3	BE	2016-12-31 03:00:00	38.265339	26.841481	31.022093	45.508585	49.689197
4	BE	2016-12-31 04:00:00	36.618801	18.541384	27.981346	45.256256	54.696218

📘 Available models in Azure AI

If you are using an Azure AI endpoint, please be sure to set model="azureai":

nixtla_client.forecast(..., model="azureai")

For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon.

By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.

nixtla_client.plot(df, timegpt_fcst_multiseries_df, max_insample_length=365, level=[80, 90])

From the figure above, we can see that the model effectively generated predictions for each unique series in the dataset.

Historical forecast

You can also compute prediction intervals for historical forecasts adding the add_history=True.

To specify the confidence interval, we use the level argument. Here, we pass the list [80, 90]. This will compute a 80% and 90% confidence interval.

timegpt_fcst_multiseries_with_history_df = nixtla_client.forecast(df=df, h=24, level=[80, 90], add_history=True)
timegpt_fcst_multiseries_with_history_df.head()

INFO:nixtlats.nixtla_client:Validating inputs...
INFO:nixtlats.nixtla_client:Preprocessing dataframes...
INFO:nixtlats.nixtla_client:Inferred freq: H
INFO:nixtlats.nixtla_client:Calling Forecast Endpoint...
INFO:nixtlats.nixtla_client:Calling Historical Forecast Endpoint...

	unique_id	ds	TimeGPT	TimeGPT-lo-80	TimeGPT-lo-90	TimeGPT-hi-80	TimeGPT-hi-90
0	BE	2016-12-06 00:00:00	55.756332	42.066476	38.185593	69.446188	73.327072
1	BE	2016-12-06 01:00:00	52.820206	39.130350	35.249466	66.510062	70.390946
2	BE	2016-12-06 02:00:00	46.851070	33.161214	29.280331	60.540926	64.421810
3	BE	2016-12-06 03:00:00	50.640892	36.951036	33.070152	64.330748	68.211632
4	BE	2016-12-06 04:00:00	52.420410	38.730554	34.849670	66.110266	69.991150

📘 Available models in Azure AI

If you are using an Azure AI endpoint, please be sure to set model="azureai":

nixtla_client.forecast(..., model="azureai")

For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon.

By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.

nixtla_client.plot(
    df, 
    timegpt_fcst_multiseries_with_history_df.groupby('unique_id').tail(365 + 24), 
    max_insample_length=365, 
    level=[80, 90],
)

In the figure above, we now see the historical predictions made by TimeGPT for each series, along with the 80% and 90% confidence intervals.