Deployed on AWS
This is a large language model finetuned for zero shot forecasting of time series data
Overview
ForecastGPT is a sophisticated AI-driven forecasting solution that leverages advanced Large Language Models (LLMs) fine-tuned on domain-specific multivariate time series datasets. It delivers unparalleled forecasting accuracy and actionable business insights, helping organizations optimize operations, plan finances, understand market dynamics, and manage human resources effectively.
ForecastGPT stands out due to its real-time adaptability and learning capabilities. Fine-tuning enhances its accuracy and reliability, offering actionable insights that help businesses stay ahead in a constantly changing landscape. With ForecastGPT, businesses can anticipate trends with unparalleled accuracy and make confident, data-driven decisions.
Highlights
- This model excels in zero shot forecasting of time series data by employing cutting-edge techniques that enable accurate predictions without the necessity of extensive historical data. It is adept at handling various data patterns, ensures rapid and reliable forecasts, and is fine-tuned to provide high performance even in complex scenarios.
- Applications of this model span across numerous fields including finance, healthcare, and supply chain management. It enables accurate demand forecasting, aids in predicting medical events, and optimizes inventory management. Its versatility and precision make it an invaluable tool for decision-making in diverse industries.
- Need more machine learning, deep learning, NLP, Generative AI and Quantum Computing solutions. Reach out to us at HARMAN DTS
Details
Delivery method
Latest version
Deployed on AWS
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Features and programs
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.4xlarge Inference (Batch) Recommended | Model inference on the ml.m5.4xlarge instance type, batch mode | $5,000.00 |
ml.m5.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.4xlarge instance type, real-time mode | $5.00 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $5,000.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $5,000.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $5,000.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $5,000.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $5,000.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $5,000.00 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $5,000.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $5,000.00 |
Vendor refund policy
We do not provide any usage-related refunds at this time
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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
Bug Fixes and feature enhancements
Additional details
Inputs
- Summary
The input is a json with first key as the time stamp and other keys as the independent variables.
- The Input Sequence Length and Prediction Length are fixed for this model. For a sequence of 96 points, the output is 16 points for future timestamps. But it can work with less than 97 points.
- The model can handle a variable number of features, which in turn does not affect the performance or time complexity of forecasting so it can handle both univariate and multivariate data.
https://github.com/HDTS-user/ForecastGPT-Multivariate/blob/main/input/ett.json
https://github.com/HDTS-user/ForecastGPT-Multivariate/blob/main/input/ett.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
date | Date and Time stamps | Type: FreeText | Yes |
Dependent Variables | The dependent variable (to be forecasted) values against the time stamps | Type: Continuous | Yes |
Independent Variables | The independent variable values against the timetamps | Default value: Null
Type: Continuous | No |
Resources
Vendor resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.comÂ
AWS infrastructure support
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