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Continuously Trained Vector Autoregressive Forecast (CTVARF) for multiple time-series influenced by common factors and hidden components.
Overview
The continuously trained vector autoregressive forecast (CTVARF) model is to forecast large set of time-series based always on the latest updated model trained by the latest available data. The time-series are assumed to be influenced by histories of a set of unobserved factors commonly affecting all or many of the time-series and by histories of hidden components affecting idiosyncratic components of the individual time-series. By applying objective data-driven constraints, the CTVARF algorithm can estimate the influences of longer histories of the unobserved common factors and hidden idiosyncratic components. Therefore, the algorithm enhances the power of machine learning. Continuous training is carried out by rolling data window moving forward and by implementation of "trained model (artifacts) feedback loop". Current version of the CTVARF algorithm estimates forecasts of common and idiosyncratic components and forecasts of the time-series; and goodness scores of the forecasts.
Highlights
- WHY “Vector Autoregression (VAR)”? Many social (trends, polls), sport (scores), economic (indicators), business (sales), natural, and engineering events can be represented quantitatively by (hourly, daily, weekly, monthly, etc.) time-series. Researches and reports demonstrate that many of these time-series interact one another directly or through underlying common factors and many time-series are influenced by their own histories as well. VAR model is the simplest model trying to find out predictive model of linear, mutual and temporal causalities underlying these time-series.
- WHY “Long Memory VAR”? The estimation engine underlying CTVARF algorithm is “long memory vector autoregressive (LMVAR)” model. The LMVAR estimates influences of longer histories of common factors and idiosyncratic components of time-series with objective data-driven constraints. Without constraints, “estimated influences of long histories” can be contaminated by various random coincidences and lack of any predictability. The data-driven constraints make CTVARF accommodate wider ranges of values of model learning parameters, and therefore further enhance the power of machine learning.
- WHY “trained model feedback loop”? To make forecasts based always on the latest updated model trained by the latest available data, we use a “continuously trained” model estimated on rolling data windows moving forward to the last time stamp of the available data. What if newly available data points arrive after training ended? With trained model feedback loop, we feed both the newly available data and the previously trained model into a new model training process. This way makes model fitting and updating efficiently by only applying them to data windows containing the new data.
Details
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Pricing
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Try this product free for 120 days according to the free trial terms set by the vendor.
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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $0.10 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.10 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $0.10 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.10 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.10 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $0.10 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
CTVARF algorithm is to forecast large set of time-series based always on the latest updated model trained by the latest available data. The time-series are influenced by unobserved common factors and hidden idiosyncratic components. Current version of the CTVARF algorithm estimates: (a) forecasts of common and idiosyncratic components of the base case (all time-series are standardized in time domain), (b) the vector time-series forecasts according to the requested specification, (c) forecasts per unit of variability of time-series, and (d) goodness scores of the vector time-series forecasts.
Additional details
Inputs
- Summary
The CTVARF algorithm takes, as input data, multiple time-series data contained in a CSV (comma separated value) data table, in a format of a CSV text-string or a CSV text-file.
Each row of the data table is for values of an individual time-series (TS). Row header is the label or symbol of the time-series. Each column is for values of all time-series at a specific moment in time. Column header is the time-index or time-stamp of the moment.
- Input MIME type
- text/csv
https://github.com/i4cast/aws/blob/main/continuously_trained_vector_autoregressive_forecast_model/input/Weekly_VTS_6Yr.csv
https://github.com/i4cast/aws/blob/main/continuously_trained_vector_autoregressive_forecast_model/input/Weekly_VTS_6Yr.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Values of time stamp | Each row of the data table is for values of an individual time-series (TS). Row header is the label or symbol of the time-series. Each column is for values of all time-series at a specific moment in time. Column header is the time-index or time-stamp of the moment. The first data column is for the earliest time and the last column for the most recent time. The current version of CTVARF requires equally spaced time-stamps. | Type: FreeText | Yes |
Support
Vendor support
For questions or call-back number, please send email to i4cast LLC at prod.i4cast@gmail.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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