Prediction of Stock Trends by Using the Wavelet Transform and the Multi-Stage Fuzzy Inference System Optimized by the GA

Yoshinori KISHIKAWA; Shozo TOKINAGA

Prediction of Stock Trends by Using the Wavelet Transform and the Multi-Stage Fuzzy Inference System Optimized by the GA

Yoshinori KISHIKAWA, Shozo TOKINAGA

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This paper deals with the prediction of stock trends by using the wavelet transform and the multi-stage fuzzy inference system based upon the optimization of membership function by using the GA. The system is expected to recognize the short-term feature which is usually used to estimate the rise/fall of price by human experts. In the prediction of stock prices, the wavelet transform is used to describe the short term feature of the stock trend. The fractal dimension and the variance of the time series are also used as the input variables. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is calculated as the ratio of the correct recognition. In the simulation study, we define the rise and fall of prices by considering the threshold value for the price change, and the interval of prediction. Then, the parameters of the system are adjusted by using the data for learning and the performance is evaluated by comparing the prediction and observation. The simulation study shows that the inference system gives about a 70% correct prediction of the price change of stocks. The result is compared to the prediction by the neural network, and we see better classification of the fuzzy system.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E83-A No.2 pp.357-366

Publication Date: 2000/02/25

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Type of Manuscript: Special Section PAPER (Special Section on Intelligent Signal and Image Processing)

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Yoshinori KISHIKAWA, Shozo TOKINAGA, "Prediction of Stock Trends by Using the Wavelet Transform and the Multi-Stage Fuzzy Inference System Optimized by the GA" in IEICE TRANSACTIONS on Fundamentals, vol. E83-A, no. 2, pp. 357-366, February 2000, doi: .
Abstract: This paper deals with the prediction of stock trends by using the wavelet transform and the multi-stage fuzzy inference system based upon the optimization of membership function by using the GA. The system is expected to recognize the short-term feature which is usually used to estimate the rise/fall of price by human experts. In the prediction of stock prices, the wavelet transform is used to describe the short term feature of the stock trend. The fractal dimension and the variance of the time series are also used as the input variables. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is calculated as the ratio of the correct recognition. In the simulation study, we define the rise and fall of prices by considering the threshold value for the price change, and the interval of prediction. Then, the parameters of the system are adjusted by using the data for learning and the performance is evaluated by comparing the prediction and observation. The simulation study shows that the inference system gives about a 70% correct prediction of the price change of stocks. The result is compared to the prediction by the neural network, and we see better classification of the fuzzy system.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_2_357/_p

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@ARTICLE{e83-a_2_357,
author={Yoshinori KISHIKAWA, Shozo TOKINAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Prediction of Stock Trends by Using the Wavelet Transform and the Multi-Stage Fuzzy Inference System Optimized by the GA},
year={2000},
volume={E83-A},
number={2},
pages={357-366},
abstract={This paper deals with the prediction of stock trends by using the wavelet transform and the multi-stage fuzzy inference system based upon the optimization of membership function by using the GA. The system is expected to recognize the short-term feature which is usually used to estimate the rise/fall of price by human experts. In the prediction of stock prices, the wavelet transform is used to describe the short term feature of the stock trend. The fractal dimension and the variance of the time series are also used as the input variables. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is calculated as the ratio of the correct recognition. In the simulation study, we define the rise and fall of prices by considering the threshold value for the price change, and the interval of prediction. Then, the parameters of the system are adjusted by using the data for learning and the performance is evaluated by comparing the prediction and observation. The simulation study shows that the inference system gives about a 70% correct prediction of the price change of stocks. The result is compared to the prediction by the neural network, and we see better classification of the fuzzy system.},
keywords={},
doi={},
ISSN={},
month={February},}

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TY - JOUR
TI - Prediction of Stock Trends by Using the Wavelet Transform and the Multi-Stage Fuzzy Inference System Optimized by the GA
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 357
EP - 366
AU - Yoshinori KISHIKAWA
AU - Shozo TOKINAGA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2000
AB - This paper deals with the prediction of stock trends by using the wavelet transform and the multi-stage fuzzy inference system based upon the optimization of membership function by using the GA. The system is expected to recognize the short-term feature which is usually used to estimate the rise/fall of price by human experts. In the prediction of stock prices, the wavelet transform is used to describe the short term feature of the stock trend. The fractal dimension and the variance of the time series are also used as the input variables. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is calculated as the ratio of the correct recognition. In the simulation study, we define the rise and fall of prices by considering the threshold value for the price change, and the interval of prediction. Then, the parameters of the system are adjusted by using the data for learning and the performance is evaluated by comparing the prediction and observation. The simulation study shows that the inference system gives about a 70% correct prediction of the price change of stocks. The result is compared to the prediction by the neural network, and we see better classification of the fuzzy system.
ER -