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.

This study aims to examine and segregate the impact of COVID-19, microfinance, and multiple macroeconomic variables on various poverty indicators in a single country at the macro level. Using a 35-year time series (1988-2022), the study applies unit root cointegration tests to address non-stationarity in the data. The semi-log regression method is employed to estimate poverty measures and disentangle the relative importance of different factors. The main research questions are: (1) What is the impact of COVID-19 on poverty indicators? (2) How does growth in microfinance borrowers, service availability and gross loan portfolio affect poverty levels? (3) What is the role of macroeconomic factors in poverty reduction? The findings reveal that the COVID-19 fixed effect is statistically significant across various poverty measures, while an increase in microfinance borrowers and service availability is associated with a reduction in poverty. The total loan portfolio has a significant effect on poverty levels despite its small size. Other macroeconomic variables have mixed effects on poverty indicators. The study concludes that governments should invest in expanding social policies such as education and training, support for entrepreneurs, and universal healthcare in addition to expanding microfinance services to reduce poverty effectively.

* The abstract needs to be concise and descriptive, and it ought to steer clear of specifying any explanatory variables. * The introduction is also very lengthy. Your introduction should be written in the order of background, problem, gap importanc...