  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.
