TIME-SERIES FORECASTING USING FEATURE BASED HYBRID APPROACH
Olashile S. Adebimpe · DalSpace (Dalhousie University) · 2019
We carried out our research using NN3 dataset and a large subset of 48,000 real-life monthly time series used in the M4 competition, which is characterized by considerable seasonality, trend and a fair amount of randomness so as to cover a wide range of time series structures. Our result reveals that the combination of decomposition, Exponential smoothing, Machine learning methods, and feature extraction gives less forecasting errors when compared to other combinatory approach and benchmark classical approach.