Entropy Application for Forecasting

<p>This book shows the potential of entropy and information theory in forecasting including both theoretical developments and empirical applications. The contents cover a great diversity of topics such as the aggregation and combination of individual forecasts the comparison of forecasting performance and the debate concerning the tradeoff between complexity and accuracy. Analyses of forecasting uncertainty robustness and inconsistency are also included as are proposals for new forecasting approaches. The proposed methods encompass a variety of time series techniques (e.g. ARIMA VAR state space models) as well as econometric methods and machine learning algorithms. The empirical contents include both simulated experiments and real-world applications focusing on GDP M4-Competition series confidence and industrial trend surveys and stock exchange composite indices among others. In summary this collection provides an engaging insight into entropy applications for forecasting offering an interesting overview of the current situation and suggesting possibilities for further research in this field.</p>