Comparative Study on Univariate Forecasting Methods for Meteorological Time Series

Abstract

Time series forecasting has an important role in many real applications in meteorology and environment to understand phenomena as climate change and to adapt monitoring strategy. This paper aims first to build a framework for forecasting meteorological univariate time series and then to carry out a performance comparison of different univariate models for forecasting task. Six algorithms are discussed: Single exponential smoothing (SES), Seasonal-naive (Snaive), Seasonal-ARIMA (SARIMA), Feed-Forward Neural Network (FFNN), Dynamic Time Warping-based Imputation (DTWBI), Bayesian Structural Time Series (BSTS). Four performance measures and various meteorological time series are used to determine a more customized method for forecasting. Through experiments results, FFNN method is well adapted to forecast meteorological univariate time series with seasonality and no trend in consideration of accuracy indices and DTWBI is more suitable as considering the shape and dynamics of forecast values.