Score Timeseries

By for October 21, 2016



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This module is to score the previously trained module 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 ### Score Time Series Module ### This module accepts serialized model as an input that is previously trained using Train/Score. This module requires only a single setting that is number of predictions to be generated. This module generates forecast data, low/high 80% confidence interval, and low/high 95% confidence interval. ![]( ### Overall Experiment ### ![]( ### Source code for modules ### The source code for this is located at [](