Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets

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• 作者：Diptendu Bhattacharya^a ; ^{diptendu1@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Amit Konar^b ; ^{konaramit@yahoo.co.in" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Pratyusha Das^b ; ^{pratyushargj@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：CSV ; Composite Secondary Variation ; CSVS ; Composite Secondary Variation Series ; FOU ; Footprint of Uncertainty ; IT2 ; Interval Type-2 ; IT2FS ; Interval Type-2 Fuzzy Set ; IT2MF ; Interval Type-2 Membership Function ; MFCP ; Main factor close price ; MFTS ; Main Factor Time-Series ; MFVS ; Main Factor Variation Series ; RMSE ; Root mean square error ; SFTS ; Secondary factor time-series ; SFVS ; Secondary Factor Variation Series ; SFVTS ; Secondary Factor Variation Time-Series ; T1 ; Type-1 ; T1FS ; Type-1 fuzzy Set ; V
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：1 January 2016
• 年：2016
• 卷：171
• 期：Complete
• 页码：551-568
• 全文大小：1867 K

文摘

The paper introduces an alternative approach to time-series prediction for stock index data using Interval Type-2 Fuzzy Sets. The work differs from the existing research on time-series prediction by the following counts. First, partitions of the time-series, obtained by fragmenting its valuation space over disjoint equal sized intervals, are represented by Interval Type-2 Fuzzy Sets (or Type-1 fuzzy sets in absence of sufficient data points in the partitions). Second, an Interval Type-2 (or type-1) fuzzy reasoning is performed using prediction rules, extracted from the (main factor) time-series. Third, a type-2 (or type-1) centroidal defuzzification is undertaken to determine crisp measure of inferences obtained from the fired rules, and lastly a weighted averaging of the defuzzified outcomes of the fired rules is performed to predict the time-series at the next time point from its current value. Besides the above three main prediction steps, the other issues considered in the paper include: (i) employing a new strategy to induce the main factor time-series prediction by its secondary factors (other reference time-series) and (ii) self-adaptation of membership functions to properly tune them to capture the sudden changes in the main-factor time-series. Performance analysis undertaken reveals that the proposed prediction algorithm outperforms existing algorithms with respect to root mean-square error by a large margin (≥23%). A statistical analysis undertaken with paired t-test confirms that the proposed method is superior in performance at 95% confidence level to most of the existing techniques with root mean square error as the key metric.