In a year where the word “forecast" has been extensively used, it's more important than ever to have accurate forecasting models. In particular, in economics, finance and business areas; forecasting techniques are used to support enterprises to decide future directions, which determine the success of the same enterprises. However, in order for the forecasting techniques to be efficient, these must be truly understood and tested in real data-driven context, by taking in account existing models and new approaches. Based on the scientific literature, the classical methodologies are the most utilised by professionals, the autoregressive moving average (e.g. ARMA) and the exponential smoothing models (e.g. ETS), are the classical methodologies which are the most utilised by professionals. Nonetheless, due to promising results, the literature has been keen on Deep Learning methodologies, in particular Deep Neural Networks (DNN). In fact, investigating what type of models should be used for each time-series based on their characteristics is the goal of this work. Three distinct models – ARMA, ETS and DNN – are assessed in the forecast of time-series with distinct patterns (see https://github.com/DidierRLopes/UnivariateTimeSeriesForecast). The discussion of the results will take into account not only the forecasting ability, but also its interpretability and computational cost. This study shows that the additional computational power required in more complex models may not justify the improved accuracy. Although in time-series with strong perturbations, advantages are recognised in DNN models (lower prediction error), in series with a clear trend and/or seasonality, classical methodologies (e.g. ETS) outweighs the former.

--

ARMA,Deep Neural Networks,ETS,Previsão,Séries Temporais

• Matemáticas - Ciências Naturais
• Ciências da Computação e da Informação - Ciências Naturais
• Economia e Gestão - Ciências Sociais