Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

Barak, Sasan; Dahooie, Jalil Heidary; Tichý, Tomáš

doi:10.1016/j.eswa.2015.08.010

Cited by 59 publications

(36 citation statements)

References 44 publications

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“…Over 90 percent of the production is for export and 10 percent of the production is for domestic consumption [ADB 2014, NESDB 2015. Therefore, it causes huge revenues and cash inflows to the country from this sector [ADB 2014, Barak et al 2015, Suganthi and Samuel 2012]. However, manufacturing sectors also has a high environmental cost as well (66%), especially energy cost as shown in Figure 1 [Hao et al 2012, Alizadeh et al 2012, Lee and Tong 2012].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Sutthichaimethee

Ariyasajjakorn

2017

J. Ecol. Eng.

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Section: Introductionmentioning

confidence: 99%

Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Sutthichaimethee

Ariyasajjakorn

2017

J. Ecol. Eng.

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“…Therefore it can interpret the obtained results, which is not possible with the structures such as neural network [21]. It is also one of the best models in estimation function among other neuro-fuzzy models [22].…”

Section: Anfismentioning

confidence: 99%

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Barak

Sadegh

2016

International Journal of Electrical Power & Energy Systems

Self Cite

269

113

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a b s t r a c tEnergy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA-ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented.The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done. IntroductionEnergy is vital important for development of every country from the social, economic and environmental perspective. It has magnificent effect on industrial and agricultural products, health, sanitary, population, education and human life quality [1].As energy is a crucial input to industrial part of country, energy demand increases along the industrial function increase. Rapid changes in industry and economy strongly affect energy consumption. Therefore, energy consumption is an important economical index that represents economic development of a city or a country [2]. According to the international energy agent report, there should be many transformations in amount and type of future energy consumption (year 2030). As over the past decade global energy consumption has increased rapidly because of population and economic growth [3,4]. According to wide growth of energy consumption in the last decade, energy demand management is very important for achieving economic success, environment preservation and suitable planning for existing resources that result in self-sufficiency and economic development. Therefore, various techniques have been used for energy demand management to forecast future energy demands accurately [4]. However, energy forecasting is difficult, because it is affected by rapid development of economy, technology, gove...

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“…Some papers [6][7][8][9][10] conclude from the experiment that the existing K-line patterns have a good forecasting capability for forecasting stock trends. Some other papers [11][12][13][14][15] have studied the stock prediction based on these patterns and have achieved some research results. However, there are also a number of papers [5,[16][17][18] challenging these patterns' predictive power.…”

Section: Introductionmentioning

confidence: 99%

K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

Hao

Shen³

2017

Mathematical Problems in Engineering

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Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1) the predictive power of a pattern varies a great deal for different shapes and (2) each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.

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Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

Cited by 59 publications

References 44 publications

Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

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