An ensemble forecasting framework for time series
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Abstract
Forecasting for businesses is essential and, because small to medium sized enterprises cannot afford to spend the resources on accurate forecasting, the necessity to
build step-by-step procedures that aid in this process is vital. Forecasting using machine learning or more complicated models comes with its own sets of challenges as
many of them have parameters that are not directly interpreted to the variables. Ensemble Forecasting is a mixture between machine learning and forecasting and it uses
many proven mathematical concepts such as the law of large numbers, the Jury theorem, and proven empirical evidence of these models outperforming the single models
counterparts. This thesis proposes a new methodology to modernize and include the
data analytics part of the cross industry standard process for data mining described in
(CRISP-DM) to the time series analysis methodology proposed by George E. Box. The ensemble methods
composed of linear combinations and majority-rule voting made better predictions and
the new Ensemble Forecast model proposed in this thesis proved to be more accurate
and precise than any other model including the other ensembling methods.