摘要：Developing predictive models is a complex task since it deals with the uncertainty and the stochastic behavior of variables. Specifically concerning commodities, accurately predicting their future prices allows us to minimize risks and establish more reliable decision support mechanisms. Although the discussion on this question is extensive, there is academic attention being paid to the construction of nonparametric models applied to energy markets, as they have presented promising predictive results, what justifies the present study. This paper applies classical statistical models and Dynamic Time Scan Forecasting (DTSF) to the short-term electricity market prices, in Brazil, from 2006 to 2019. DTSF consists of scanning a time series and then identifying past patterns (so-called “matches”), similar to the last available observations. We predict Brazilian electricity spot prices, according the most similar matches, using aggregation functions, such as median. Recent research on the electricity spot market is increasing, indicating research significance. Our predictive approach exhibited greater accuracy than seminal statistical models. Our approach was designed for a high frequency series. Its predictive performance remained robust when other models presented both high predictive errors (spring), as well as when those models are highly accurate (winter). For future research, we recommend a more finely-tune study on DTSF parameters.
其他摘要：Developing predictive models is a complex task since it deals with the uncertainty and the stochastic behavior of variables. Specifically concerning commodities, accurately predicting their future prices allows us to minimize risks and establish more reliable decision support mechanisms. Although the discussion on this question is extensive, there is academic attention being paid to the construction of nonparametric models applied to energy markets, as they have presented promising predictive results, what justifies the present study. This paper applies classical statistical models and Dynamic Time Scan Forecasting (DTSF) to the short-term electricity market prices, in Brazil, from 2006 to 2019. DTSF consists of scanning a time series and then identifying past patterns (so-called “matches”), similar to the last available observations. We predict Brazilian electricity spot prices, according the most similar matches, using aggregation functions, such as median. Recent research on the electricity spot market is increasing, indicating research significance. Our predictive approach exhibited greater accuracy than seminal statistical models. Our approach was designed for a high frequency series. Its predictive performance remained robust when other models presented both high predictive errors (spring), as well as when those models are highly accurate (winter). For future research, we recommend a more finely-tune study on DTSF parameters.
