Evaluate Timeseries

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  This module generates plot of actual vs. forecast for users to visualize effectiveness of the predictions  This module also produces performance metrics such as root mean square for measuring effectiveness and quality of the model  ### Overall Experiment ###  ### Source code for modules ### The source code for this is located at [https://gist.github.com/nk773/9cbbc1bd8856ef958451baa1803c5eaf](https://gist.github.com/nk773/9cbbc1bd8856ef958451baa1803c5eaf)