Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

Men, Baohui; Long, R. W.; Li, Yangsong; Liu, Huanlong; Tian, Wei; Wu, Zhijian

doi:10.3390/e19120694

Cited by 5 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A school can be regarded to be a complex system in an educational setting, and entropy is abound in every complex system [3]. If researchers could predict conditions of complex weather systems by utilizing the concept of entropy [4][5][6][7], wouldn't it also be possible to harness entropy to work for educational stakeholders to predict conditions and outcomes in the future? Specifically, "would it be possible to predict conditions that could enhance student performance, when there could be dynamic confounding factors with parameters that could change?"…”

Section: Research Problem and Research Questionsmentioning

confidence: 99%

Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

How

Hung

2019

Education Sciences

View full text Add to dashboard Cite

Educational stakeholders would be better informed if they could use their students’ formative assessments results and personal background attributes to predict the conditions for achieving favorable learning outcomes, and conversely, to gain awareness of the “at-risk” signals to prevent unfavorable or worst-case scenarios from happening. It remains, however, quite challenging to simulate predictive counterfactual scenarios and their outcomes, especially if the sample size is small, or if a baseline control group is unavailable. To overcome these constraints, the current paper proffers a Bayesian Networks approach to visualize the dynamics of the spread of “energy” within a pedagogical system, so that educational stakeholders, rather than computer scientists, can also harness entropy to work for them. The paper uses descriptive analytics to investigate “what has already happened?” in the collected data, followed by predictive analytics with controllable parameters to simulate outcomes of “what-if?” scenarios in the experimental Bayesian Network computational model to visualize how effects spread when interventions are applied. The conceptual framework and analytical procedures in this paper could be implemented using Bayesian Networks software, so that educational researchers and stakeholders would be able to use their own schools’ data and produce findings to inform and advance their practice.

show abstract

Section: Research Problem and Research Questionsmentioning

confidence: 99%

Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

How

Hung

2019

Education Sciences

View full text Add to dashboard Cite

show abstract

“…In this section of the paper, we compare the performance of the proposed DWP estimator with other methods used to combine individual forecasts by carrying out a numerical simulation study. Forecast combinations have been successfully applied in several areas of forecasting, such as economy (gross valued added, inflation, or stock returns), meteorology (wind speed, rainfall, see e.g., [ 47 ] in Entropy journal), or energy fields (wind power), among others. We focus our empirical exercise in the economic area; in fact, we take variable

as the gross value added being forecasted.…”

Section: A Numerical Simulation Studymentioning

confidence: 99%

A Data-Weighted Prior Estimator for Forecast Combination

Vázquez

Moreno

Hewings

2019

Entropy

View full text Add to dashboard Cite

Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good forecasters, even in the situation of little information. With this purpose, the data-weighted prior (DWP) estimator proposed by Golan (2001) is used for forecaster selection and simultaneous parameter estimation in linear statistical models. In particular, we examine the ability of the DWP estimator to effectively select relevant forecasts among all forecasts. We test the accuracy of the proposed model with a simulation exercise and compare its ex ante forecasting performance with other methods used to combine forecasts. The obtained results suggest that the proposed method dominates other combining methods, such as equal-weight averages or ordinal least squares methods, among others.

show abstract

“…Unlike the conventional clustering methods [ 11 , 12 ], multi-manifold clustering can be classified into two different categories, the linear method and the nonlinear method [ 13 ]. In the first category, linear methods (also known as subspace clustering) construct the multi-manifold clustering by assuming that the underlying cluster can be well approximated by a union of low dimensional linear manifolds [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency

Zhao

Chen

2018

Entropy

View full text Add to dashboard Cite

Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generative clustering approach within the Bayesian nonparametric framework has been proposed. Specifically, our manifold method automatically selects the cluster number with a Dirichlet Process (DP) prior. Then, a DP-based mixture model with constrained Mixture of Gaussians (MoG) is constructed to handle the manifold data. Finally, we integrate our model with the k-nearest neighbor graph to capture the manifold geometric information. An efficient optimization algorithm has also been derived to do the model inference and optimization. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new DP-based manifold method.

show abstract

Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

Cited by 5 publications

References 23 publications

Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

A Data-Weighted Prior Estimator for Forecast Combination

A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency

Contact Info

Product

Resources

About