Last updated: Feb 03, 2025
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.
Note:
The time series forecast method of the watsonx.ai API is available as a beta feature.
Ways to develop
You can generate forecasts from time series foundation models by using these programming methods:
Overview
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 based on historic observations from 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
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 dataset.ibm/granite-ttm-1024-96-r2: Requires at least 1,024 data points per dataset.ibm/granite-ttm-1536-96-r2: Requires at least 1,536 data points per dataset.
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 target column. For best results, use the model that takes the most data points based on the data available to you.
For more information, see the model card.
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'
In the example, replace the {url} variable with the right value for your instance, such as us-south.ml.cloud.ibm.com.
Python SDK
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:
Sample Python notebook
Use Time Series Foundation Models and timeseries data to predict energy demand
REST API
Use the time series forecast method of the watsonx.ai REST API to forecast future values based on historical data.
For details, see the API reference documentation.
REST API data requirements
A time series is a collection of data points that are collected over time.
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 dataset. 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.
-
Each dataset must have an equal number of items in the arrays that are specified for the
date,id_columns, andtarget_columnsthat are included in the request body. You cannot skip a data point or specifynullas 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 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 duplicate 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
freqparameter.For example, if you specify a frequency of one day (
"freq":"1D"), your time stamps probably look something like this:"date": [ "2024-11-15T15:06:35", "2024-11-16T15:06:35", ... ]If the frequency is 5 minutes (
"freq":"5min"), each date time stamp is likely 5 minutes apart."date": [ "2024-11-15T15:06:35", "2024-11-15T15:11:35", ... ]For supported values for date and time abbreviations in the frequency parameter, see Period aliases in the documentation of 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 dataset 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 datasets, A and B. Dataset A shows the sales per day for location A and dataset B shows the sales per day for location B.
Table 1. Sample sales data Date Dataset 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:
... "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, 63788, 41855, 105678, ... ] } -
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 datasets. One dataset shows the sales per day and the other dataset 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. When the data from the table is submitted to the API, it is formatted as follows:
... "data": { "date": [ "2024-12-07T00:00:00", "2024-12-07T00:00:00", "2024-12-08T00:00:00", "2024-12-08T00:00:00", ... ], "ids": [ "sales", "returns", "sales", "returns", ... ], "sales": [ 63788, 105678, ... ], "returns": [ 14788, 25678 ... ] }
REST API example
This example uses the granite-ttm-1536-96-r2 model to forecast energy generation for different types of energy sources per hour from 1 January 2015 to 5 March 2015. The dataset used in the example tracks the energy generation for
a European country and is sourced from the European Network of Transmission System Operators for Electricity (ENTSO-E) website. 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 production for each of the three energy sources
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.
- Data: The data to be analyzed.
The schema section looks like this:
"schema": {
"timestamp_column": "date",
"freq":"1h",
"target_columns": [
"gas",
"nuclear",
"solar"
]
},
You might notice that there is no id_columns object included in the schema. In this example, data from only one dataset 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 dataset, you must include the id_columns object to identify the source of each data point.
The data section includes information about the energy production per hour from various energy sources as shown in the following table.
| Date and time (date) | Natural gas energy (gas) | Nuclear energy (nuclear) | Solar energy (solar) |
|---|---|---|---|
| 2015-01-01T00:00:00 | 4844 | 7096 | 49 |
| 2015-01-01T01:00:00 | 5196 | 7096 | 50 |
| 2015-01-01T02:00:00 | 4857 | 7099 | 50 |
The granite-ttm-1536-96-r2 model requires at least 1,536 data points per dataset 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 for each energy source.
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.
REST API request example
curl -X POST \
'https://{url}/ml/v1/time_series/forecast?version=2025-01-10' \
--header 'Accept: application/json' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer eyJraWQiOi...' \
--data '{
...
}'
The data payload contains the following JSON snippet:
{
"model_id":"ibm/granite-ttm-1536-96-r2",
"project_id":"4ab1437e-4c9d-47cf-9926-9ab52db1ee01",
"schema": {
"timestamp_column": "date",
"freq":"1h",
"target_columns": [
"gas",
"nuclear",
"solar"
]
},
"data": {
"date": [
"2015-01-01T00:00:00",
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}
}
REST API response example
The following response is returned for the example request.
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"created_at": "2025-01-10T19:14:43.971Z",
"results": [
{
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}
]
}
REST API example results
The time series model predicted future values for energy production for each of the three energy sources over the next 96 hours.
The model is typically able to correctly forecast the general trend in values, including spikes in output. However, the model's predicted numbers tend to be more conservative than the actual numbers.
The graph in the following screenshot shows the forecasted natural gas energy production compared to the actual production over that same time frame.
The graph in the following screenshot shows the forecasted solar energy production compared to the actual production over that same time frame.
Learn more
Parent topic: Coding generative AI solutions