Time Series Analysis and Its Applications

By R. Shumway, David S. Stoffer. Springer Texts in Statistics, 2025

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Correlated daily time series and forecasting in the M4 competition (2020, methodology): “First, we use the classical decomposition (Macaulay, 1931) instead of STL decomposition (Cleveland et al., 1990) and secondly, we use either linear extrapolation (Shumway & Stoffer, 2017) or past values from the decomposition for forecasting trend and residuals.”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, methodology): “We also evaluated the performance of both forecasting methodologies in producing forecast intervals that usually provide coverages which are close to the nominal rates [23].”
Analysis of photovoltaic system performance time series: Seasonality and performance loss (2015, methodology): “Due to the recursive semi-parametric nature of ARIMA modeling, statistical software was essential in order to calculate model coefficients [23,33,45].”