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Entropy Application for Forecasting

Entropy Application for Forecasting
Author: Ana Jesus Lopez-Menendez
Publisher: MDPI
Total Pages: 200
Release: 2020-12-29
Genre: Technology & Engineering
ISBN: 3039364871
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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.

Entropy Application for Forecasting

Entropy Application for Forecasting
Author: Ana Jesus Lopez-Menendez
Publisher:
Total Pages: 200
Release: 2020
Genre:
ISBN: 9783039364886
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Download Entropy Application for Forecasting Book in PDF, ePub and Kindle

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.

Forecasting with Maximum Entropy Hb

Forecasting with Maximum Entropy Hb
Author: FORT
Publisher: IOP ebooks
Total Pages: 0
Release: 2022-11-30
Genre: Entropy (Information theory)
ISBN: 9780750339292
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This book aims at providing a unifying framework, based on Information Entropy and its maximization, to connect the phenomenology of evolutionary biology, community ecology, financial economics, and statistical physics. This more comprehensive view, besides providing further insight into problems, enables problem-solving strategies by applying proven methods in one discipline to formally similar problems in other areas. The book also proposes a forecasting method for important practical problems in these disciplines and is directed to researchers, students and practitioners working on modelling the dynamics of complex systems. The common thread is how the flux of information both controls and serves to predict the dynamics of complex systems. It is shown how maximizing the Shannon information entropy allows one to infer a central object controlling the dynamics of complex systems, such as ecosystems or markets. The resulting models, which are known as pairwise maximum-entropy models, can be used to infer interactions from data in a wide variety of systems. Here, two examples are analysed in detail. The first is an application to conservation ecology, namely the issue of providing early warning indicators of population crashes of species of trees in tropical forests. The second is about forecasting the market values of firms through evolutionary economics. An interesting lesson is that PME modelling often produces accurate predictions despite not incorporating explicit interaction mechanisms. Key features Written to be suitable for a broad spectrum of readers and assumes little mathematical specialism. Includes pedagogical features: Worked examples, case studies and summaries. The interdisciplinary approach builds bridges between disciplines. Oriented to solve practical problems. Includes a combination of analytical derivations and numerical simulations with experiments

A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy

A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy
Author: Hongjun Guan
Publisher: Infinite Study
Total Pages: 15
Release:
Genre: Mathematics
ISBN:
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Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange CapitalizationWeighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality.

Forecasting with Maximum Entropy

Forecasting with Maximum Entropy
Author: Jack K. Hutson
Publisher:
Total Pages: 11
Release: 1984
Genre: Maximum entropy method
ISBN:
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Entropy Applications in Environmental and Water Engineering

Entropy Applications in Environmental and Water Engineering
Author: Huijuan Cui
Publisher: MDPI
Total Pages: 512
Release: 2019-03-07
Genre: Technology & Engineering
ISBN: 3038972223
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Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Rényi, Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.

A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation
Author: Hongjun Guan
Publisher: Infinite Study
Total Pages: 18
Release:
Genre: Mathematics
ISBN:
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In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data.

Entropy Theory for Streamflow Forecasting

Entropy Theory for Streamflow Forecasting
Author: Huijuan Cui
Publisher:
Total Pages:
Release: 2015
Genre:
ISBN:
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Entropy spectral analysis is developed for monthly streamflow forecasting, which contains the use of configurational entropy and relative entropy. Multi-channel entropy spectral analysis is developed for long-term drought forecasting with climate indicators. The configurational entropy spectral analysis (CESA) is developed with both spectral power and frequency as random variables. With spectral power as a random variable, the configurational entropy spectral analysis (CESAS) identical to the original Burg entropy spectral analysis (BESA) when the underlying process is Gaussian. Through examination using monthly streamflow from the Mississippi Watershed, CESAS and BESA yield the same results and two methods are considered equivalent or as one method. With frequency as a random variable, the configurational entropy spectral analysis (CESAF) is developed and tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CESAF captures streamflow seasonality and satisfactorily forecasts both high and low flows. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately than downstream streamflow. Minimum relative entropy spectral analysis (MRESA) is developed under two conditions: spectral power as a random variable (RESAS) and frequency as a random variable (RESAF). The exponential distribution was chosen as a prior probability in the RESAS theory, and in the RESAF theory, the prior is chosen from the periodicity of streamflow. Both MRESA theories were evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflow shows higher reliability in the Upper Mississippi than in the Lower Mississippi. The proposed univariate entropy spectral analyses are generally recommended over the classical autoregressive (AR) process for higher reliability and longer forecasting lead time. By comparing two MRESA theories with the two maximum entropy spectral analyses (MESA) (BESA and CESA), it is found that MRESA provided higher resolution in spectral estimation and more reliable streamflow forecasting, especially for multi-peak flow conditions. The MRESA theory is more accurate in forecasting streamflow for both peak and low flow values with longer lead time than MESA. Besides, choosing frequency as a random variable shows advantages over choosing spectral power. Spectral density estimated by the RESAF or CESAF theory shows higher resolution than the RESAS or BESA theory, respectively, and streamflow forecasted by RESAF or CESAF is more reliable than that by RESAS or BESA, respectively. Finally, multi-channel entropy spectral analysis (MCESA) is developed for bivariate or multi-variate time series forecasting. MCESA theory is verified by forecasting long-term standardized streamflow index with El Nino Southern Oscillation (ENSO) indicator. SSI was successfully forecasted using multi-channel spectral analysis with ENSO as an indicator. The monthly drought series is forecasted for lead times of 4-6 years by MCESA. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155060