Use the IBM Granite time series foundation models and forecast API to forecast future values
Last updated: Feb 12, 2025
Use the IBM Granite time series foundation models and forecast API to forecast future values
Use the time series forecast API and IBM Granite time series models that are available from watsonx.ai to forecast future values based on historic data.
Ways to develop
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You can generate forecasts from time series foundation models by using these programming methods:
Use the time series forecast API to pass historical data observations to a time series foundation model that can forecast future values with zero-shot inferencing. For example, you can use this method to forecast future values from time-based
observations of the following types of data:
Stock share prices and trading volumes
Electrocardiogram (EKG) data or polysomnogram (PSG) records
Temperature or seismographic data
Network performance measurements
Supported foundation models
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IBM Granite time series foundation models with the following model IDs are available to use for forecasting in watsonx.ai:
ibm/granite-ttm-512-96-r2: Requires at least 512 data points per channel in the request.
ibm/granite-ttm-1024-96-r2: Requires at least 1,024 data points per channel in the request.
ibm/granite-ttm-1536-96-r2: Requires at least 1,536 data points per channel in the request.
The Granite time series models are compact pretrained models for multivariate time series forecasting from IBM Research, also known as Tiny Time Mixers (TTM).
The Granite time series models work best with data points in minute or hour intervals and generate a forecast dataset with 96 data points per channel by default. For best results, use the model that takes the most data points based on the data
available to you.
To programmatically get a list of the time series models that are available for use, you can use the List the available foundation models method in the watsonx.ai
as a service API. Specify the filters=function_time_series_forecast parameter to return only the available time series models.
For example:
curl -X GET \
'{url}/ml/v1/foundation_model_specs?version=2024-11-14&filters=function_time_series_forecast'
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In the example, replace the {url} variable with the right value for your instance, such as us-south.ml.cloud.ibm.com.
Python SDK
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You can write code that uses the time series models to forecast data by using the watsonx.ai Python library. For more information, see the following references:
To use the Granite time series models to forecast future values, the data that you submit for analysis must meet the following requirements:
The recorded data must be numerical, such as temperatures or stock share prices.
Your data must include enough data points of historical context for the model to be able to make a prediction. Each time series foundation model specifies a required minimum number of data points per channel in the request. If you specify
more than the required number, the model uses the most recent data points up to the model requirement and ignores the rest.
There must be an equal number of items in the arrays that are specified for the date, id_columns, and target_columns that are included in the request body. You cannot skip a data point or specify null as the data point. Check your data before you submit the request.
Although the service accepts most common date and time formats, when you specify dates with time stamps in ISO 8601 format (2024-11-12T15:06:35), you avoid the confusion that can arise due to differences in date formatting conventions.
For example, does 11/12/2024 mean November 12 or December 11? Specify dates in ISO 8601 format with the coordinated universal time (UTC) offset (2024-11-12T15:06:35+0000) to avoid apparent duplicates or missing timestamps
in the generated forecasting results.
Sample the data at a uniform frequency. For example, data can be observed in increments of 1 minute, 1 hour, or 1 day. No error is generated if your timestamps are not uniform, but the quality of your results might be poor. The generated
forecast data is also formatted with the frequency that you specify with the freq parameter.
For example, if you specify a frequency of one day ("freq":"1D"), your time stamps probably look something like this:
For supported values for date and time abbreviations in the frequency parameter, see Period aliases in
the documentation for the pandas library.
If you include data points from more than one source for analysis, each data point must provide a unique identifier to indicate which series, or collection of observations, it belongs to.
For example, if you are analyzing shopping trends, you might be interested in sales figures from multiple stores during the holiday season. The following table shows two series, A and B. Series A shows the sales per day for location A and
series B shows the sales per day for location B.
Table 1. Sample sales data
Date
Series ID
Sales total (USD)
7 December 2023
A
$24,988
7 December 2023
B
$63,788
8 December 2023
A
$41,855
8 December 2023
B
$105,678
...
...
...
Although the table shows observations from two days, your request must include between 512 to 1,536 data points, depending on the model you use. When the data from the table is submitted to the API, it is formatted as follows:
You can forecast by using multivariate data points, meaning data points that measure different factors during the same time interval.
For example, you might be interested in both sales and returns from one store during the holiday season. The following table shows a few entries from two channels, or different dimension or aspects of the recorded data. One channel
shows the sales per day and the other channel shows the returns per day.
Table 2. Sample sales and returns data
Date
Sales (USD)
Returns (USD)
7 December 2023
$63,788
$14,788
8 December 2023
$105,678
$25,678
...
...
...
Although the table shows observations from two days, your request must include between 512 to 1,536 data points, depending on the model you use. The id_column field is omitted because the data is coming from the same series, a collection
of observations from a single store. When the data from the table is submitted to the API, it is formatted as follows:
You can forecast by using multivariate data points from more than one series of observations. For example, you can effectively combine the previous two scenarios to forecast sales and returns for both store A and store B.
The data in the request might look something like this:
"data": {
"date": [
"2024-12-07T00:00:00",
"2024-12-07T00:00:00",
"2024-12-08T00:00:00",
"2024-12-08T00:00:00",
...
],
"ids": [
"A",
"B",
"A",
"B",
...
],
"sales-usd": [
24988, // sales for store A on 7 December 2024
63788, // sales for store B on 7 December 2024
41855,
105678,
...
],
"returns": [
3997, // returns for store A on 7 December 2024
14788, // returns for store B on 7 December 2024
5768,
25678,
...
]
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Billing information
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Charges for foundation model forecasting are calculated by using the following equations:
Input calculation: context length x number of series x number of channels
Output calculation: prediction length x number of series x number of channels
These equations use the following values:
Context length refers to the number of historical data points that a time series foundation model uses as input to make predictions.
For example, when the context length is 1536, the model uses the past 1536 data points, such as stock prices over the past 1536 days, to make predictions. The context length is determined by the foundation model that is used
for forecasting.
A series is a collection of observations made sequentially over time.
For example, when comparing stock prices for many companies, the observed stock price history for each company is a separate series. The set of series to be measured is specified in the ID_column segment of the API request body.
Channels are specific features or variables within a time series dataset. In multivariate time series data, each channel represents a different dimension or aspect of the recorded data.
For example, in a stock price dataset, channels might include opening price, closing price, volume, and moving averages. Each measured variable represents a separate channel. The channels to be measured are specified in the target_columns segment of the API request body.
Prediction length is the number of future data points for the model to predict. When the requested prediction length is 96, the model forecasts the next 96 data points.
For example, the model predicts stock prices for the next 96 days. The prediction length can be specified by including the prediction_length parameter in the API request.
This example uses the granite-ttm-1536-96-r2 model to forecast total energy demand for the country of Portugal per hour from 30 September 2024 to 2 December 2024. The dataset used in the example is sourced from the European Network of Transmission System Operators for Electricity (ENTSO-E) website. To see the data, go to this page on the ENTSO-E site, select Portugal as the country, and then choose 29 September 2024 as the date with the default time zone (CET).
The API request does not specify a prediction_length parameter. Therefore, the default length for the foundation model is used, which means the time series model will predict future values for energy demand over the next 96 hours.
The request body is divided into two sections:
Schema: Specifies which datasets will be included and which fields to analyze.
You might notice that there is no id_columns object included in the schema. In this example, data from only one series of observed data is being submitted, which means the id_columns object can be omitted from the schema.
However, if your request includes data from more than one source, you must include the id_columns object to identify the source of each data point.
The data section includes information about the energy demand per hour for the country of Portugal as shown in the following table.
Table 3. Sample energy consumption data
Date and time (date)
Total energy demand (MW)
2024-09-30T00:00:00
4941
2024-09-30T01:00:00
4640
2024-09-30T02:00:00
4443
The granite-ttm-1536-96-r2 model requires at least 1,536 data points per channel in the request to ensure that there is enough data for the model to predict future values. Although the table shows 3 data points, the request body contains 1,536
data points.
In the following example, replace the {url} variable with the right value for your instance, such as us-south.ml.cloud.ibm.com. Add your own bearer token and project ID.
The data payload contains the following JSON snippet:
{"model_id":"ibm/granite-ttm-1536-96-r2","project_id":"51f3a990-4372-4ac3-9ddb-ed99d9b50840","schema":{"freq":"1h","timestamp_column":"Time (CET/CEST)","target_columns":["Actual Total Load [MW]"]},"data":{"Time 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Total Load 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REST API response example
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The following response is returned for the example request.
{"model_id":"ibm/granite-ttm-1536-96-r2","created_at":"2025-02-05T14:00:53.597Z","results":[{"Actual Total Load [MW]":[6465.83154296875,5847.07080078125,5351.2109375,5033.18212890625,4876.1064453125,4780.388671875,4784.177734375,4964.26171875,5416.59375,6017.84619140625,6571.35986328125,6776.26318359375,6817.2890625,6812.52001953125,6746.37744140625,6707.9755859375,6773.8466796875,6847.83154296875,6995.001953125,7263.904296875,7553.798828125,7598.19580078125,7379.0302734375,6916.89697265625,6339.57373046875,5786.80908203125,5321.87109375,5020.8017578125,4870.2451171875,4778.8359375,4778.078125,4953.40869140625,5410.39306640625,6020.509765625,6577.892578125,6781.26220703125,6819.33056640625,6812.33154296875,6745.134765625,6713.60888671875,6793.95068359375,6881.67236328125,7031.44482421875,7290.23681640625,7568.93115234375,7616.61572265625,7406.73779296875,6966.48828125,6415.345703125,5881.96533203125,5431.17919921875,5135.4541015625,4989.001953125,4898.27001953125,4896.9013671875,5056.8369140625,5490.11767578125,6091.61865234375,6649.93603515625,6870.30419921875,6938.3583984375,6951.720703125,6891.03173828125,6848.82275390625,6900.1689453125,6957.650390625,7084.47900390625,7316.27734375,7567.8828125,7598.6650390625,7385.21875,6948.34619140625,6407.689453125,5887.63427734375,5447.83349609375,5159.2392578125,5006.68359375,4896.49267578125,4857.845703125,4971.42236328125,5354.44921875,5898.3193359375,6420.6103515625,6639.318359375,6716.24462890625,6734.14306640625,6672.8359375,6624.00634765625,6670.64453125,6729.83056640625,6862.82861328125,7105.01708984375,7353.390625,7373.8955078125,7164.09912109375,6736.27734375],"Time (CET/CEST)":["2024-12-02T23:00:00","2024-12-03T00:00:00","2024-12-03T01:00:00","2024-12-03T02:00:00","2024-12-03T03:00:00","2024-12-03T04:00:00","2024-12-03T05:00:00","2024-12-03T06:00:00","2024-12-03T07:00:00","2024-12-03T08:00:00","2024-12-03T09:00:00","2024-12-03T10:00:00","2024-12-03T11:00:00","2024-12-03T12:00:00","2024-12-03T13:00:00","2024-12-03T14:00:00","2024-12-03T15:00:00","2024-12-03T16:00:00","2024-12-03T17:00:00","2024-12-03T18:00:00","2024-12-03T19:00:00","2024-12-03T20:00:00","2024-12-03T21:00:00","2024-12-03T22:00:00","2024-12-03T23:00:00","2024-12-04T00:00:00","2024-12-04T01:00:00","2024-12-04T02:00:00","2024-12-04T03:00:00","2024-12-04T04:00:00","2024-12-04T05:00:00","2024-12-04T06:00:00","2024-12-04T07:00:00","2024-12-04T08:00:00","2024-12-04T09:00:00","2024-12-04T10:00:00","2024-12-04T11:00:00","2024-12-04T12:00:00","2024-12-04T13:00:00","2024-12-04T14:00:00","2024-12-04T15:00:00","2024-12-04T16:00:00","2024-12-04T17:00:00","2024-12-04T18:00:00","2024-12-04T19:00:00","2024-12-04T20:00:00","2024-12-04T21:00:00","2024-12-04T22:00:00","2024-12-04T23:00:00","2024-12-05T00:00:00","2024-12-05T01:00:00","2024-12-05T02:00:00","2024-12-05T03:00:00","2024-12-05T04:00:00","2024-12-05T05:00:00","2024-12-05T06:00:00","2024-12-05T07:00:00","2024-12-05T08:00:00","2024-12-05T09:00:00","2024-12-05T10:00:00","2024-12-05T11:00:00","2024-12-05T12:00:00","2024-12-05T13:00:00","2024-12-05T14:00:00","2024-12-05T15:00:00","2024-12-05T16:00:00","2024-12-05T17:00:00","2024-12-05T18:00:00","2024-12-05T19:00:00","2024-12-05T20:00:00","2024-12-05T21:00:00","2024-12-05T22:00:00","2024-12-05T23:00:00","2024-12-06T00:00:00","2024-12-06T01:00:00","2024-12-06T02:00:00","2024-12-06T03:00:00","2024-12-06T04:00:00","2024-12-06T05:00:00","2024-12-06T06:00:00","2024-12-06T07:00:00","2024-12-06T08:00:00","2024-12-06T09:00:00","2024-12-06T10:00:00","2024-12-06T11:00:00","2024-12-06T12:00:00","2024-12-06T13:00:00","2024-12-06T14:00:00","2024-12-06T15:00:00","2024-12-06T16:00:00","2024-12-06T17:00:00","2024-12-06T18:00:00","2024-12-06T19:00:00","2024-12-06T20:00:00","2024-12-06T21:00:00","2024-12-06T22:00:00"]}],"input_data_points":1536,"output_data_points":96}
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REST API example results
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The time series model predicted future values for energy demand in Portugal over the next 96 hours.
The graph in the following screenshot shows the forecasted energy demand compared to the actual energy demand over that same time frame.