Rainfall and financial forecasting using fuzzy time series and neural networks based model

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.

A Deep and Wide Neural Network-based Model for Rajasthan Summer Monsoon Rainfall (RSMR) Prediction

“…Fluctuations in the summer monsoon rainfalls can't be captured efficiently by traditional linear statistical models [69,13]. This motivated us to use Deep Learning based model which are efficient in capturing this non-linearity and dynamic nature of ISMR.…”

“…The problem of predicting summer monsoon rainfall in Rajasthan is different from the prediction of Indian summer monsoon rainfall (ISMR, hereafter). Most of the time-series-based methods for predicting ISMR consider average monthly rainfall values by taking weighted average of the 306 well distributed raingauge stations in the non-hilly areas of Indian sub-continent [13,69,70,62]. Rajasthan being a dry state lies in arid and semi-arid zones and characterized by low and uneven rainfall [38], therefore, a dedicated system is required which can predict monsoon rainfall for different geographical regions separately.…”

Neural Network-based System for Automatic Passport Stamp Classification

“…It learns and exhibits the capability for generalization beyond the training data. ANNs are being used as classifier tools in various fields such as medical diagnosis [5,7], financial forecasting [29,42] and signal and image classification [33,48].…”

“…It learns and exhibits the capability for generalization beyond the training data. ANNs are being used as classifier tools in various fields such as medical diagnosis [5,7], financial forecasting [29,42] and signal and image classification [33,48]. In the literature, there are many types of neural network architecture; we focus on the Multi-layer perceptron (MLP) [1,27], that its basic model is shown in Figure 8.…”

Demand Forecasting Approaches Based on Associated Relationships for Multiple Products

“…As the development of artificial intelligence, various machine learning models have been widely used in the area of forecasting, including neural network [ 41 , 42 , 43 ], support vector machine [ 44 , 45 , 46 ], nearest neighbor regression [ 47 , 48 ], and so on. They are serious contenders to classical statistical models and form a vital research branch.…”