  Many businesses and industries require accurate forecasts for weekly time
series nowadays. However, the forecasting literature does not currently provide
easy-to-use, automatic, reproducible and accurate approaches dedicated to this
task. We propose a forecasting method in this domain to fill this gap,
leveraging state-of-the-art forecasting techniques, such as forecast
combination, meta-learning, and global modelling. We consider different
meta-learning architectures, algorithms, and base model pools. Based on all
considered model variants, we propose to use a stacking approach with lasso
regression which optimally combines the forecasts of four base models: a global
Recurrent Neural Network model (RNN), Theta, Trigonometric Box-Cox ARMA Trend
Seasonal (TBATS) and Dynamic Harmonic Regression ARIMA (DHR-ARIMA), as it shows
the overall best performance across seven experimental weekly datasets on four
evaluation metrics. Our proposed method also consistently outperforms a set of
benchmarks and state-of-the-art weekly forecasting models by a considerable
margin with statistical significance. Our method can produce the most accurate
forecasts, in terms of mean sMAPE, for the M4 weekly dataset among all
benchmarks and all original competition participants.
