For2For: Learning to forecast from forecasts

January 15, 2020 Abstract This paper presents a time series forecasting framework which combines standard forecasting methods and a machine learning model. The inputs to the machine learning model are not lagged values or regular time series features, but instead forecasts produced by standard methods. The machine learning model can be either a convolutional neural network model or a recurrent neural network model. The intuition behind this approach is that forecasts of a time series are themselves good features characterizing the series, especially when the modelling purpose is forecasting. It can also be viewed as a weighted ensemble method. Tested on the M4 competition dataset, this approach outperforms all submissions for quarterly series, and is more accurate than all but the winning algorithm for monthly series. 1 Introduction The competitiveness of neural network (NN) models and other machine learning (ML) models for time series forecasting compared to statistical models has long been questioned by practitioners [1] [2]. Although in the field of time series forecasting, there is a plethora of literature presenting complex novel models, in practice the performance of ML models is often below expectation [3].