Forecasting without context problem

This work presents an analysis of four regression systems. Two of them are statistical: the widely used Auto-regressive Integrated Moving Average (ARIMA) and the state-of-the-art Facebook Prophet. From the deep learning school, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is also evaluated. We finish our quartet with a fine-tuned Nearest Neighbor model. The study is carried out over seventeen benchmarks; fifteen coming from M4-Competition and two more power systems time series, i.e., electricity demand and hydropower generation. For all the models, the regression systems are fitted and optimized to minimize user intervention. The results show that deep learning models obtained the best performance; nonetheless, the performance difference is not statistically significant with the rest of the systems tested.