Forecasting without context problem

Abstract

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.

Predictive Tools as Part of Decission Aiding Processes at the Airport – The Case of Facebook Prophet Library

“…Such approach can be found e.g. in (Ortiz-Bejar et al, 2020;Papacharalampous et al, 2018). These metrics give a chance to compare results presented in this work to results obtained by other researchers.…”

“…MAPE metric measures the forecasting accuracy of a model. MAPE was described by Ortiz-Bejar et al, (2020) as easy to interpret, scale-independent, and one of the most widely used to measure regression systems performance. It is expressed by…”