Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index

Das, Shom Prasad; Padhy, Sudarsan

doi:10.1007/s12293-016-0191-4

Cited by 23 publications

(7 citation statements)

References 28 publications

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“…ANNs is outstanding among the techniques used with this purpose. The ELM algorithm has been reported to be computationally efficient [47], [81], has high accuracy, fast prediction speed and clear superiority in financial market prediction [21], [82]- [84]. Therefore, we use an ANN whose training procedure is the ELM algorithm to forecast the next period stock returns.…”

Section: ) Calculating the Evaluation Of Desirability Of Stock S Jmentioning

confidence: 99%

A Comprehensive Soft Computing-Based Approach to Portfolio Management by Discarding Undesirable Stocks

et al. 2022

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Stock markets play a crucial role in modern society. Many individuals and organizations try to improve their performance in these markets by exploiting approaches that consider different types of factors. However, although there are great complexities involved, practitioners often use simplistic methodologies that neglect relevant features of the problem. That is, they leave out some activities that are essential to obtain better results in the related decision-making problem. Here, we propose to use a soft computing-based approach to comprehensively address the main activities of stock investments. We present a novel method for modeling expert knowledge through fuzzy logic that allows the investor to discard undesirable stocks (i.e., stocks that are not suitable for investment); thus, reducing computational complexity in the search process and likely improving the performance of the final stock portfolio. Extensive experiments allowed us to conclude that discarding undesirable stocks by exploiting the proposed method produces portfolios that outperform the benchmarks. Therefore, the proposal is a promising alternative to complement current approaches.

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Section: ) Calculating the Evaluation Of Desirability Of Stock S Jmentioning

confidence: 99%

A Comprehensive Soft Computing-Based Approach to Portfolio Management by Discarding Undesirable Stocks

et al. 2022

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“…Clust-Dist was calculated through clustering of the entire spatially adjusted G-BLUEs time series (of each trait) using the Gaussian Kernel K-Means clustering method (Dhillon et al, 2004;Steinbach et al, 2004). To determine the optimal number of genotypic clusters, the Silhouette method (Rousseeuw, 1987;Das and Padhy, 2017) was used. After determining the optimal number of clusters, genetic diversity values between those clusters were estimated by calculating the Euclidean distance between the cluster centers at each day (Dhillon et al, 2004).…”

Section: Stage 3: Temporal Analysis Of G-bluesmentioning

confidence: 99%

SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

et al. 2020

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The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

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“…One of such kernel is a Gaussian kernel (RBF). Due to its implacable functionality, it can help the learning machine to enhance its classification or regression capability [10,11]. So, RBF-ELM can be a fruitful method to implement the analog circuit modeling.…”

Section: Proposed Techniquementioning

confidence: 99%

A Novel Modeling Technique for Operational Amplifier Using RBF - ELM

Patro¹,

Mandal²

2016

JESTR

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Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index

Cited by 23 publications

References 28 publications

A Comprehensive Soft Computing-Based Approach to Portfolio Management by Discarding Undesirable Stocks

A Comprehensive Soft Computing-Based Approach to Portfolio Management by Discarding Undesirable Stocks

SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

A Novel Modeling Technique for Operational Amplifier Using RBF - ELM

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