Entropy Application for Forecasting
Lopez-Menendez, Ana Jesus
Entropy Application for Forecasting - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (200 p.)
Open Access
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.
Creative Commons
English
books978-3-03936-488-6 9783039364879 9783039364886
10.3390/books978-3-03936-488-6 doi
Economics, finance, business & management
Entropy Application for Forecasting - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (200 p.)
Open Access
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.
Creative Commons
English
books978-3-03936-488-6 9783039364879 9783039364886
10.3390/books978-3-03936-488-6 doi
Economics, finance, business & management