Combining Forecasts: A Genetic Programming Approach

2. Related Works

In time series analysis, aiming in univariate forecasting, we can find several approaches traditionally focusing on mathematical and statistical methods (Atsalakis & Valavanis, 2009; Stepnicka et al., 2013). In this way, forecasting models like naive, linear and nonlinear models and Holt-Winters (Hyndman et al., 2008; Taylor, 2012) are mathematical methods that use extracted components (trend, seasonality, etc.) or past samples of the underlying process as a way to produce forecasts. Although, a time series can also be understood as a stochastic process that follows a specific probability distribution. By this principle, regression analysis and autoregressive integrated with moving average (ARIMA) models (Box et al., 1994; Valenzuela et al., 2008) are adequate tools that provide good performance if the time series follows all the statistical assumptions, for example, the time series follows a Gaussian distribution.