Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Chen, Tai‐Liang; Cheng, Ching‐Hsue; Teoh, Hia Jong

doi:10.1016/j.physa.2007.02.084

Cited by 102 publications

(53 citation statements)

References 23 publications

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“…Pada FTS yang digunakan adalah himpunan fuzzy sebagai suatu kelas bilangan dengan batasan yang samar, atau dengan kata lain, prediksi dalam sistem fuzzy yang digunakan bukan nilai riil melainkan nilai linguistik. Dengan kata lain metode FTS tidak hanya bergantung pada asumsi data stasioner terhadap ragam ataupun rata-rata, sedangkan metode time series konvensional lainnya membutuhkan lebih banyak data historis yang menyebar normal (Chen et al,, 2007).…”

Section: Pendahuluanunclassified

Implementasi Metode Fuzzy Time Series Cheng untuk prediksi Kosentrasi Gas NO2 Di Udara

Fathoni

2017

J. Sistem Info. Bisnis

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The forecasting process is essential for determining air quality to monitor NO2 gas in the air. The research aims to develop prediction information system of NO2 gas in air. The method used is Fuzzy Time Series Cheng method. The process of acquiring NO2 gas data is integrated with Multichannel-Multistasion. The data acquisition process uses Wireless Sensor Network technology via broadband internet that is sent and stored in an online database form on the web server. Recorded data is used as material for prediction. Acquisition result of NO2 gas data is obtained from the sensor which is sent to the web server in the data base in the network by on line, then for futher it is predicted using fuzzy time series Cheng applying re-divide to the results of intervals the first partition of the value of the universe of discourse by historical data fuzzification to determine Fuzzy Logical Relationship dan Fuzzy Logical Relationship Group, so that is obtained result value prediction of NO2 gas concentration. By using 36 sample data of NO2 gas, it is obtained that the value of root of mean squared error of 2.08%. This result indicates that the method of Fuzzy Time Series Cheng is good enough to be used in predicting the NO2 gas.Keywords: Fuzzy Time Series Cheng; Fuzzy Time Series Cheng; gas NO2 AbstrakProses peramalan sangat penting untuk penentuan kualitas udara untuk memantau gas NO2 di udara. Penelitian ini bertujuan mengembangkan sistem informasi prediksi gas NO2 di udara. Metode yang digunakan adalah metode Fuzzy Time Series Cheng. Proses akuisisi data gas NO2 terintegrasi dengan Multichannel-Multistasion. Proses akuisi data ini menggunakan teknologi Wireless Sensor Network melalui internet broadband yang dikirimkan dan disimpan dalam bentuk online database pada web server. Data yang terekam digunakan sebagai bahan untuk prediksi. Data gas NO2 hasil akuisisi diperoleh dari sensor yang dikirim ke web server dalam basis data dalam jaringan secara online untuk selanjutnya di prediksi menggunakan Fuzzy Time Series Cheng yang menerapkan pembagian ulang terhadap hasil interval partisi pertama dari nilai semesta pembicaraandengan melakukan fuzzifikasi data historis untuk menentukan Fuzzy Logical Relationship dan Fuzzy Logical Relationship Group, sehingga didapat hasil nilai prediksi konsentrasi gas NO2. Dengan menggunakan 36 data sampel dari gas NO2, diperoleh nilai root of mean squared error sebesar 2.08%. Hal ini mengindikasikan bahwa metode Fuzzy Time Series Cheng adalah cukup baik untuk digunakan dalam memprediksi Gas NO2.Kata Kunci : Fuzzy Time Series Cheng; Fuzzy Time Series Cheng; gas NO2 Pendahuluan.Prediksi Data runtun waktu (time series) adalah jenis data yang dikumpulkan menurut urutan waktu dalam suatu rentang waktu tertentu. Analisis data runtun waktu merupakan salah satu prosedur statistika yang diterapkan untuk meramalkan struktur probabilitas keadaan yang akan datang dalam rangka pengambilan keputusan. Pemodelan time series dapat untuk memprediksi kecenderungan perkembangan atau perubahan suatu obyek di...

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Section: Pendahuluanunclassified

Implementasi Metode Fuzzy Time Series Cheng untuk prediksi Kosentrasi Gas NO2 Di Udara

Fathoni

2017

J. Sistem Info. Bisnis

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“…To improve the fuzzy time series model in constructing fuzzy relationships, forecasting and defuzzifing the forecasting output, Chen [21] modified the models by using simple algebraic operation in [1][2][3]. Chen introduced the Fibonacci sequence into the fuzzy time series, and testified the validity of the model [22]. Lee presented some high-order models based on two-factors and genetic simulated annealing techniques [23].…”

Section: Introductionmentioning

confidence: 99%

Generalized Fuzzy Time Series Forecasting Model Enhanced with Particle Swarm Optimization

Qiu¹,

Zhang²,

Zhang³

2015

IJUNESST

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1.IntroductionSince fuzzy time series model was proposed by Song and Chissom [1][2][3] in 1993, there are many forecasting models have been developed to deal with the forecasting problems due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. In the literatures, the experimental analysis existing in many areas, such as forecasting stock price [4][5], tourism demand[6], temperature [7], amount of export [8] and dry bulk shipping index [9], etc.Most of the exiting methods mainly focused on partitioning the universe of discourse, constructing fuzzy relationships from the fuzzy set, forecasting and defuzzifing the forecasting output. A proper choice of the length of each interval can greatly improve the forecasting results. Huarng [10,11] presented some methods to obtain the proper lengths of the intervals for the fuzzy time series model. Chen proposed the automatic clustering technique to generate clustering-based intervals for some fuzzy time series forecasting models [12,13].Literatures [14,15]introduced an approach which using an optimization technique with a single-variable constraint to determine an optimal interval length. The particle swarm optimization algorithm and some other algorithms were used to search the appropriate intervals on the fuzzy time series [16][17][18][19] as well geneticalgorithms [20]. To improve the fuzzy time series model in constructing fuzzy relationships, forecasting and

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“…In stock index prediction, some works deal with level forecasting using fuzzy logic (Huarng, 2001a(Huarng, , 2001bYu, 2005aYu, , 2005bHuarng & Yu, 2005, 2006Chen, Cheng & Teoh, 2007Chu et al, 2009) and hybrid models with rough sets (Teoh et al, , 2009, Markov chain concept (Wang, Cheng & Hsu, 2010), or neural networks (Cheng, Wei & Chen, 2009;Yu & Huarng, 2008Boyacioglu & Avci, 2010), but none of them deals with forecasts on the direction/sign of the changes in price levels. In addition, according to Leung, Daouk, & Chen (2000), forecasting strategies based on levels seem to be less profitable than forecasting strategies based on the change of direction/sign of a stock index.…”

Section: Introductionmentioning

confidence: 99%

S&P 500 Index Direction Forecasting from 1976 to 2010: A Fuzzy System Approach

Souto-Maior¹,

Borba²,

Costa³

2011

IJDAR

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Abstract. Investors are not always completely rational and they do not always work only with numbers. Sometimes, they use linguistic concepts to make their decisions. Fuzzy logic is helpful in handling such situations. This article differs from other studies in this area by applying fuzzy logic for forecasting the direction/sign of stock market indexes. The article also presents a model for forecasting the direction/sign of stock market indexes using a fuzzy system, and this model is applied in the prediction of the S&P 500 Index. The rules of the model were established between 1970 and 2009, and the test period was from 1976 to 2010. Despite the fact that the estimated model produced a linguistic output, it was possible to delineate a statistically significant investment strategy, which outperformed a buy-and-hold one. In addition, the success rate calculated was also statistically significant. Another difference from other studies is that the proposed model does not return an exact output, but a probabilistic one of linguistic variables. The proposed model, with its probabilistic output, can be used as a support for investment decisions. In other words, the linguistic output does not force the investor to blindly follow the proposed strategy.

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Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Cited by 102 publications

References 23 publications

Implementasi Metode Fuzzy Time Series Cheng untuk prediksi Kosentrasi Gas NO2 Di Udara

Implementasi Metode Fuzzy Time Series Cheng untuk prediksi Kosentrasi Gas NO2 Di Udara

Generalized Fuzzy Time Series Forecasting Model Enhanced with Particle Swarm Optimization

S&P 500 Index Direction Forecasting from 1976 to 2010: A Fuzzy System Approach

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