Neural network ensemble operators for time series forecasting

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Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “tistical forecasting methods (Adya and Collopy, 1998; Zhang et al., 1998; Crone et al., 2011). Though some works have shown that NNs can be competitive even in situations where few data is available (Kourentzes et al., 2014a; Trapero et al., 2012), they typically realize their full potential in situ”
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016, methodology): “However, in this study we restrict our analysis to the median, as it achieves good results and is less sensitive to outliers than, e.g., the mean, also taking into account the results of Kourentzes et al. (2014a).”
Forecasting with temporal hierarchies (2017, background): “Finally Section 7 demonstrates their use and benefits in a case study of Accident & Emergency departments, followed by concluding remarks.”
A comparison of AdaBoost algorithms for time series forecast combination (2016, background): “Surprisingly, Boosting research on time series has neglected simpler approaches of equally weighted combinations (Kourentzes et al. 2014), with only thresholdbased Boosting (Avnimelech and Intrator, 1999) using a simple median for combination.”