Train Score Timeseries

By for October 21, 2016

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This module trains a model for forecasting timeseries data using ARIMA, STL, ETS, or STL+ARIMA
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 ### Train Score Time Series Module ### As shown below, this module accepts a single input, a training dataset, used to train the time series data. This module requires user to configure additional settings specifying column to be used for training the model, number of predictions to be generated, user's POV for seasonality of input data, and algorithm to be used for training module. This module generates forecast data, low/high 80% confidence interval, low/high 95% confidence interval and serialized model to be used for scoring only. Use this module to train time series forecast model. During operationalization, if your timeseries data will change frequently, then use this module to train on new timeseries data instead of just score time series module which works on previously trained model ![]( ### Overall Experiment ### ![]( ### Source code for modules ### The source code for this is located at [](