An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting

Yang, Ruixin; He, Jue; Xu, Mingyang; Ni, Haoqi; Jones, Paul; Samatova, Nagiza F.

doi:10.1007/978-3-319-95786-9_8

Cited by 16 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of using FTS models and, as a consequence, our sci-FTS software is noticed in several application domains. By performing a literature review, it is possible to realize that the modeling of time series yielded from stock markets using sci-FTS is firmly suitable due to the natural presence of imprecision and uncertainty in their operations [8][9][10][11][12]. In summary, these manuscripts forecast new values by performing the three steps implemented in sci-FTS: Fuzzification, Fuzzy Logic Relationship (FLR), and Defuzzification.…”

Section: Impact Overviewmentioning

confidence: 99%

sci-FTS: Using soft clustering on Intrinsic Mode Functions to model Fuzzy Time Series

Ferreira

Rios

2022

Software Impacts

View full text Add to dashboard Cite

introduces a new software, sci-FTS, which models time series by combining Signal Processing tools and Fuzzy Set Theory. Firstly, time series are decomposed into Intrinsic Mode Functions (IMFs), emphasizing instantaneous frequencies and amplitudes. Secondly, sci-FTS combines IMFs to extract deterministic influences, removing noises. Next, sci-FTS executes an algorithm that finds an adequate space partitioning to produce the fuzzy sets. Finally, Fuzzy Time Series steps are considered to predict observations. Our contributions are twofold: sci-FTS finds out similar patterns in observations to better model the universe of discourse; ii) models produced by sci-FTS overcome studies from the literature.

show abstract

Section: Impact Overviewmentioning

confidence: 99%

sci-FTS: Using soft clustering on Intrinsic Mode Functions to model Fuzzy Time Series

Ferreira

Rios

2022

Software Impacts

View full text Add to dashboard Cite

show abstract

“…Learning Optimization e HLO [28] algorithm adopts the three learning operators, i.e., the random learning operator (RLO), the individual learning operator (ILO), and the social learning operator (SLO), to search for the optimal solution. Nowadays, HLO has been successfully used to solve the various types of optimization problems, such as furnace flame recognition [16], image segmentation [26], knapsack problems [29], engineering design problems [27,30], optimal power flow calculation [31], extractive text summarization [32], financial markets forecasting [33], scheduling problems [34], and intelligent control [35]. To solve the mixed variables of NACMM more effectively, an adaptive strategy is developed to further enhance the search ability of AHcHLO.…”

Section: Adaptive Hybrid-coded Humanmentioning

confidence: 99%

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid‐Coded HLO

et al. 2021

View full text Add to dashboard Cite

In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.

show abstract

“…To further extend HLO, a novel hybrid-coded HLO (HcHLO) [ 39 ] is proposed to tackle mix-coded problems, in which real-coded parameters are optimized by a new continuous HLO (CHLO) [ 39 ] and the binary and discrete variables are handled by the binary learning operators of HLO. Until now, HLO has been successfully applied to engineering design problems [ 37 ], knapsack problems [ 40 ], optimal power flow calculation [ 41 ], extractive text summarization [ 42 ], financial markets forecasting [ 43 ], furnace flame recognition [ 44 ], scheduling problems [ 45 ], and intelligent control [ 46 ]. In particular, HLO obtained the best-so-far results on two well-studied sets of multidimensional knapsack problems, i.e., 5.100 and 10.100 [ 40 ], as well as the set of mixed-variable optimization problems [ 39 ] which implies the promising advantages of HLO.…”

Section: Introductionmentioning

confidence: 99%

Differential Human Learning Optimization Algorithm

Zhang

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.

show abstract

An Intelligent and Hybrid Weighted Fuzzy Time Series Model Based on Empirical Mode Decomposition for Financial Markets Forecasting

Cited by 16 publications

References 40 publications

sci-FTS: Using soft clustering on Intrinsic Mode Functions to model Fuzzy Time Series

sci-FTS: Using soft clustering on Intrinsic Mode Functions to model Fuzzy Time Series

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid‐Coded HLO

Differential Human Learning Optimization Algorithm

Contact Info

Product

Resources

About