Empirical analysis: stock market prediction via extreme learning machine

Li, Xiaodong; Xie, Haoran; Wang, Ran; Cai, Yi; Cao, Jingjing; Wang, Feng; Min, Huaqing; Deng, Xiaotie

doi:10.1007/s00521-014-1550-z

Cited by 127 publications

(68 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To support our use of the SVR technique for the forecasting task instead of ELM regression, we conducted an empirical study of a hybrid model using US-ELM clustering and ELM regression (hybrid USELM-ELM). Our results support the findings of Huang et al [15] and Li et al [21].…”

Section: Introductionsupporting

confidence: 93%

“…Moreover, empirical results obtained from a comparison of SVR and ELM regression by Huang et al [15] confirmed that the SVR prediction is more accurate than the ELM. According to the experimental results of Li et al [21], the SVR outperformed the ELM when predicting stock market data. To support our use of the SVR technique for the forecasting task instead of ELM regression, we conducted an empirical study of a hybrid model using US-ELM clustering and ELM regression (hybrid USELM-ELM).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Das

Padhy²

2016

Memetic Comp.

View full text Add to dashboard Cite

Support vector regression (SVR) has been successfully applied in various domains, including predicting the prices of different financial instruments like stocks, futures, options, and indices. Because of the wide variation in financial time-series data, instead of using only a single standard prediction technique like SVR, we propose a hybrid model called USELM-SVR. It is a combination of unsupervised extreme learning machine (US-ELM)-based clustering and SVR forecasting. We assessed the feasibility and effectiveness of this hybrid model using a case study, predicting the one-, two-, and three-day ahead closing values of the energy commodity futures index traded on the Multi Commodity Exchange in India. Our experimental results show that the USELM-SVR is viable and effective, and produces better forecasts than our benchmark models (standard SVR, a hybrid of SVR with self-organizing map (SOM) clustering, and a hybrid of SVR with k-means clustering). Moreover, the proposed USELM-SVR architecture is useful as an alternative model for prediction tasks when we require more accurate predictions.

show abstract

Section: Introductionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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

Das

Padhy²

2016

Memetic Comp.

View full text Add to dashboard Cite

show abstract

“…We follow an event‐based inflow, as used in Li, et al (). This is due to the fact that events (i.e., orders, executions, and cancellations) do not follow a uniform inflow rate.…”

Section: The Lob Datasetmentioning

confidence: 99%

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Ntakaris

Magris

Kanniainen

et al. 2018

Journal of Forecasting

115

106

View full text Add to dashboard Cite

Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high‐frequency limit order markets for mid‐price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of ∼4,000,000 time series samples in total. A day‐based anchored cross‐validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state‐of‐the‐art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large‐scale dataset can serve as a testbed for devising novel solutions of expert systems for high‐frequency limit order book data analysis.

show abstract

“…The MAF algorithm easy to use but reduces performance when using for noisy data. Li et al (2016) built a new trading-mining platform for prediction of stock-market. For speed and accuracy the author used back extreme learningmachine (BELM) and Kernelized extreme learningmachine (KELM) for collection hidden information in raw data which predict price-movements.…”

Section: Literature Reviewmentioning

confidence: 99%

Pakistan stock exchange prediction using RIDOR classifier

Akram¹,

Imran

2017

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

Now-a-days stock market prediction is important activity and interesting topic for professional analyst. The stock market is biggest investment platform, however investment in stock market need accurate and complete information. The accurate and fast-prediction of stock market attracted the investor for profitable output. The stock market prediction is complex task because uncertainty involves in the market movement up/down. Mostly the machine learning techniques (MLT) are used for accurate prediction of stock market, because of its capability of partitioning; extract hidden information form raw data, monitors the fluctuation rate of stock market, suitable for nonlinear data etc. This research work is about to review the strength and weakness of existing stock market prediction techniques. This research work proposed a Ripple-down-rule-learner (RIDOR) classifier based technique. The RIDOR rule base classifier generates default value and work like if-else statement for uncertainties. The other contribution is a prepared data-set using technical indicator to predict stock market trend. The output of the propose model is outperformed as compare to the exiting techniques.

show abstract

Empirical analysis: stock market prediction via extreme learning machine

Cited by 127 publications

References 27 publications

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

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

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Pakistan stock exchange prediction using RIDOR classifier

Contact Info

Product

Resources

About