Time series is a classification of data series of variables, orderly arranged with respect to time. In time series analysis, forecasting is the vital area of study besides other meaningful characteristics of the data. It has vast application in decision-making and prediction in the domain of economics, agriculture, medicine, industries, energy sector and other sciences. Fuzzy time series emerged as a robust tool to cater for historical data in linguistic values. This paper proposes the new method of fuzzy time series forecasting based on the approach of fuzzy clustering and information granules integrated with the weighted average approach to deal with the uncertainty in data series. To distinguish the power of modeling and prediction, the strategies of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized as a criterion. Findings illustrate that proposed fuzzy based time series approach is vigorous to compute the accurate estimates.

Fish as a source of acoustic signal measurement in an aquaculture tank: acoustic sensor based time frequency analysis

A time series is a sequence of observations that a variable takes with respect to times. It has a wide range of applications in decision making and forecasting in economics, agriculture, medicine, industry, energy sector and other scientific fields. Time series modeling and forecasting contain some of the classical issues that are widely addressed in the literature based on traditional statistical models with low interpretability. Fuzzy time series has become a powerful tool that can counter the problem of prediction of historical data in linguistic terms. This study proposes a new framework for modeling the fuzzy time series approach in the environment of intuitionistic fuzzy set theory to play viable role in ensuring robustness to the uncertainty involved in data series. In order to get the optimized length of intervals, the principles of fuzzy c-means (FCM) clustering and information granules are integrated. To fuzzify the historical data, intuitionistic fuzzy triangular function is practiced to acquire the intuitionistic fuzzy sets. Furthermore, the distance measures between the elements of the intuitionistic fuzzy set of the fuzzified historical data and the centers of the corresponding clusters are computed for all fuzzy sets. Finally, a robust fuzzy time series model is designed by extracting fuzzy logical relationships and employing weighted association reasoning as an exhaustive defuzzification approach. The parameters of accuracy measures such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used to identify the strength of the proposed modeling and forecasting. Findings demonstrate that the proposed forecasting method is robust in determining the highly accurate forecasts.

The precise estimates about finance, atmospheric science, power sector, industries, agriculture, and other science help governments and institutions economically in making the relevant policies regarding import-export, demand, consumption, storage, and local industries. Due to the uncertainty and nondeterministic behavior of data series with respect to time, the foremost challenge is to develop and identify the practical method to handle the above stated complex issues. As an illustration, this study presented an analysis of a new fuzzy time-series (FTS) approach and comparison with traditional forecasting models for prediction of gram pulse production. Taking into consideration the theory of fuzzy sets, FTS, fuzzy rules, triangular membership functions, distance measures, and modified weighted average method, a robust and effective fuzzy rules-based methodology was developed for the prediction of time-series data regarding crop production and share prices. Conventional statistical forecasting methods such as Holt’s linear trend, Holt’s exponential trend, and Holt’s damped exponential trend models were also applied on time-series data for comparison. To identify the primacy of modeling and forecasting, the techniques of root mean squared error (RMSE) and mean absolute error (MAE) were used as a criterion. The numerical values of RMSE and MAE such as 106.51 and 74.8897 clearly demonstrated that the proposed fuzzy rules-based method is robust for forecasting of production and market share prices in the environment of uncertainty.

The main objective of this paper is to present an integrated approach to evaluate the level of satisfaction of borrowers with the products and services of microfinance institutions (MFI) at different criterion levels. For this, the study adopts the concept of FCEM (Fuzzy Comprehensive Evaluation Method) in concurrence with the AHP (Analytical Hierarchy Process). In our day-to-day situation, the researchers have made many efforts to assess the impact of Microfinance on poverty reduction, but borrowers’ satisfaction is always overlooked. Since the multiple factors impact the borrower’s satisfaction, each factor is made of different items. Thus, as the factors items increase, many uncertainties are created, and hence this will make the decision making unsmooth or imprecise. To describe this, the FCEM method deals with the vagueness in the collection information phase. However, the AHP has been utilized to determine the objective weights of each factor. The presented integrated framework has been illustrated with a case study and presented their results. The study’s managerial benefit is also reported to address the situation.