Time Series Forecasting in Orange

We have been discussing on Time series for while now with different perspectives. In this journey, we are in the process of building the Cheat Sheet for Time Series that covers all aspects of Time Series. Earlier this month, a new cheat sheet version 4.5.0.1 was released. 

On this journey, we were looking at the features of the time series in different tools. We looked at the features of Microsoft SQL Server, Azure Machine Learning and WEKA till now. In this post, we are looking at the Time Series forecasting features in Orange.

Let us look at how we can Time Series Forecasting in Orange as shown in the below figure. 

In this example, the plane passenger data set was used where date as the time column while the value as the numeric, forecasting column. You can Select Columns and Select Rows as data cleaning techniques. 

Then the data set can be converted to a Time Series by using the As TimeSeries control. Then the shifting of data can be done by Difference control. Seasonal Adjustment control can be utilized to include the seasonal factor in the time series as the seasonal factor plays a key role in Time Series forecasting. 

In Orange, ARIMA modelling is available where there are few configurations to be done as shown below. 

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In ARIMA modelling, we have indicated that there are will be four predictions with 95 confidence intervals. 

Finally, model evaluation is done in order to select the better Time Series Model. In the Model Evaluation, root mean squared error (RMSE), median absolute error (MAE), mean absolute per cent error (MAPE), prediction of change in direction (POCID), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) parameters are used. 

Following are the model evaluation parameters for the selected ARIMA model.

By changing the ARIMA models, it was found that the best model is ARIMA(1,1,0).

Apart from the above features, there are other features like windows slicing, Spiralogram, Aggregate, Interpolate features that are also available.