Forecasting - ETS + STL API

By for April 22, 2015

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Fit an Exponential Smoothing (ETS) and Seasonal Trend Decomposition (STL) model to predict values in the future.
> **Note:** This is depreciated. Forecasting - ETS + STL API is an example built with Microsoft Azure Machine Learning that fits a ETS + STL model to time series data input by the user and outputs forecasted values. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends and seasonality to predict future trends. This web service implements Seasonal Trend decomposition (STL) and Exponential Smoothing model (ETS) to produce predictions based on the historical data provided by the user. It can be used for any time series and can make predictions into the future. However, note that forecasting for the near future tends to be more reliable. *While this web service could be consumed by users � potentially through a mobile app, website, or even on a local computer for example, the purpose of the web service is also to serve as an example of how Azure ML can be used to create web services on top of R code. With just a few lines of R code and clicks of a button within the Azure ML Studio, an experiment can be created with R code and published as a web service. The web service can then be published to the Azure Marketplace and consumed by users and devices across the world with no infrastructure set-up by the author of the web service.* *While this web service could be consumed by users � potentially through a mobile app, website, or even on a local computer for example, the purpose of the web service is also to serve as an example of how Azure ML can be used to create web services on top of R code. With just a few lines of R code and clicks of a button within the Azure ML Studio, an experiment can be created with R code and published as a web service. The web service can then be published to the Azure Marketplace and consumed by users and devices across the world with no infrastructure set-up by the author of the web service.*