Evaluate Timeseries

By for October 21, 2016



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This module evaluates timeseries forecast generated using "Train Score Time Series" module
This experiment has 3 new modules that helps create forecast for time series data - **Train and Score time series** data using R time series library. This module asks users to provide dataset with historical values, provide number of forecast points, seasonality period, and forecast algorithm (Arima, ETS, STL) - **Scoring time series** accepts input as serialized model with number of forecast periods. This module forecasts future periods based on the model and requested number of periods - **Evaluate time series** - this accepts dataset with observed and forecast values to generate performance metrics such as RMSE and plot actual vs. forecast ### Evaluate Time Series Module ### This module accepts dataset containing actual vs. forecast values. This module expects user to configure column representing actual, column representing forecast, and prediction algorithm ![](http://neerajkh.blob.core.windows.net/images/EvaluateCapture.PNG) This module generates plot of actual vs. forecast for users to visualize effectiveness of the predictions ![](http://neerajkh.blob.core.windows.net/images/PlotForecastActualCapture.PNG) This module also produces performance metrics such as root mean square for measuring effectiveness and quality of the model ![](http://neerajkh.blob.core.windows.net/images/perfmetrics.png) ### Overall Experiment ### ![](http://neerajkh.blob.core.windows.net/images/timeseries.png) ### Source code for modules ### The source code for this is located at [https://gist.github.com/nk773/9cbbc1bd8856ef958451baa1803c5eaf](https://gist.github.com/nk773/9cbbc1bd8856ef958451baa1803c5eaf)