Sold by: IBM Data and AIÂ
Deployed on AWS
IBM Granite TimeSeries TTM is a compact pre-trained model with less than 1 million parameters for multivariate time-series forecasting.
Overview
IBM's Granite TimeSeries TTM, also known as TinyTimeMixer, is a compact pre-trained model for multivariate time-series forecasting, containing less than 1 million parameters. Despite its small size, TTM outperforms several popular benchmarks that require billions of parameters in both zero-shot and few-shot forecasting scenarios. It is pre-trained on publicly available time-series datasets (~700M samples) and can be fine-tuned with minimal data to enhance performance. The current open-source version supports point forecasting use cases with resolutions ranging from minutely to hourly intervals (e.g., 10 minutes, 15 minutes, 1 hour). Notably, zero-shot, fine-tuning, and inference tasks using TTM can be efficiently executed on a single GPU machine or even on laptops, making it accessible for a wide range of users. The model is released under the Apache 2.0 license.
Highlights
- Despite having fewer than 1M parameters, the IBM Granite TimeSeries TTM outperforms larger models requiring billions of parameters in both zero-shot and few-shot forecasting tasks. TTM supports point forecasting across minutely to hourly intervals and is optimized for efficiency, allowing fine-tuning and inference on a single GPU or even a laptop. Its compact yet powerful design makes it ideal for scalable, real-world time-series applications.
- The IBM Granite TimeSeries TTM model is developed following IBM's AI Ethics principles, leveraging high-quality public time-series datasets with diverse augmentations to enhance forecasting accuracy. It is designed for accessibility and efficiency, enabling responsible AI use in time-series applications. Released under the Apache 2.0 license, TTM is available for both research and commercial use.
- The IBM Granite TimeSeries TTM model is designed for multivariate time-series forecasting across various domains. It supports point forecasting at different time resolutions, from minutely to hourly intervals, making it adaptable to a wide range of real-world applications. With strong zero-shot and fine-tuning capabilities, TTM enables businesses and researchers to develop precise, efficient forecasting models without requiring extensive computational resources.
Details
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Latest version
Deployed on AWS
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Pricing
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.c4.xlarge Inference (Batch) Recommended | Model inference on the ml.c4.xlarge instance type, batch mode | $0.00 |
ml.c4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.c4.xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $0.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.00 |
ml.c4.8xlarge Inference (Batch) | Model inference on the ml.c4.8xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This current version is the TTM-R2 model built upon the success of TTM-R1, offering enhanced performance with a larger training dataset of 700M samples.
Additional details
Inputs
- Summary
The model can be invoked by passing time-series data. Please see the sample notebook for details.
- Input MIME type
- application/json
https://github.com/ibm-granite-community/SageMaker/blob/main/granite-timeseries-ttm-r2/real_time_sample_input_data.json
https://github.com/ibm-granite-community/SageMaker/blob/main/granite-timeseries-ttm-r2/batch_sample_input_data.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
inference_type | Must contain the value: "forecasting" | Type: FreeText | Yes |
model_id | The model to be used for generating a forecast.
Must contain the value: "ttm-r2" | Type: FreeText
Limitations: 1 ≤ length ≤ 256, Value must match regular expression ^\S+$ | Yes |
schema.timestamp_column | A valid column in the data that should be treated as the timestamp. Although not absolutely necessary, if using calendar dates (simple integer time offsets are also allowed), users should consider using a format such as ISO 8601 that includes a UTC offset (e.g., '2024-10-18T01:09:21.454746+00:00'). There are many date formats in existence and inferring the correct one can be a challenge so please do consider adhering to ISO 8601. | Type: FreeText
Limitations: 1 ≤ length ≤ 100, Value must match regular expression ^\S.*\S$ | Yes |
data | A payload of data matching schema (this input must be an object, not a string). We assume the following about your data:
- All timeseries are of equal length and are uniform in nature (the time difference between two successive rows is constant). This implies that there are no missing rows of data;
- The data meet the minimum model-dependent historical context length which can be 512 or more rows per timeseries; | Type: FreeText
Limitations: This input must be an object, not a string. | Yes |
Resources
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