Rainfall and financial forecasting using fuzzy time series and neural networks based model
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ORIGINAL ARTICLE
Rainfall and financial forecasting using fuzzy time series and neural networks based model Pritpal Singh1
Received: 6 January 2015 / Accepted: 12 May 2016 Springer-Verlag Berlin Heidelberg 2016
Abstract In this study, the author presents a new model to deal with four major issues of fuzzy time series (FTS) forecasting, viz., determination of effective lengths of intervals (i.e., intervals which are used to fuzzify the numerical values), repeated fuzzy sets, trend associated with fuzzy sets, and defuzzification operation. To resolve the problem of determination of length of intervals, this study suggests the application of an artificial neural network (ANN) based algorithm. After generating the intervals, the historical time series data set is fuzzified based on FTS theory. In part of existing FTS models introduced in the literature, each fuzzy set is given equal importance, which is not effective to solve real time problems. Therefore, in this model, it is recommended to assign weights on the fuzzy sets based on their frequency of occurrences. In the FTS modeling approach, fuzzified time series values are further used to establish the fuzzy logical relations (FLRs). To determine the trends associated with the fuzzy sets in the corresponding FLR, this article also introduces three trend-based conditions. To deal repeated fuzzy sets and trend associated with them, this study proposes a new defuzzification technique. The proposed model is verified and validated with real-world time series data sets. Empirical analyzes signify that the proposed model has the robustness to deal one-factor time series data sets very efficiently than existing FTS models. Experimental results show that the proposed model also outperforms over the conventional statistical models.
& Pritpal Singh [email protected]; [email protected] 1
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT Campus, Anand 388421, Gujarat, India
Keywords Fuzzy time series (FTS) Artificial neural network (ANN) Fuzzification Indian summer monsoon rainfall (ISMR)
1 Introduction Fuzzy time series (FTS) is a modeling approach, in which historical values of time series are used to forecast the future values by employing fuzzy set concept. Forecasting using FTS is applied to several areas, including forecasting university enrollments [3], sales, road accidents and financial forecasting [7, 8, 12, 53]. In a conventional time series, the recorded values of a special dynamic process are represented by crisp numerical values. However, in a FTS model, the recorded values of a special dynamic process are represented by linguistic values. Based on FTS concept, first forecasting model was introduced by [52]. They presented the FTS model by means of fuzzy relational equations involving max-min composition operation and applied the model to forecast the enrollments in the University of Alabama. In 1996, Chen [9] used simplified arithmetic operations, avoiding the complicated max-min operations and their method pro
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