Fuzzy Computing in Data Science

6Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model

S. Mala* and V. Umadevi

Department of Computer Science, Nehru Memorial College (Affiliated to Bharathidasan University), Tiruchirapalli, India

Abstract

This chapter focuses on prediction-based sales data analysis with four types of seeds sample, such as spinach, black gram, gingelly, and fodder sorghum. The proposed work has applied a fuzzy logic technique, namely clustering and artificial intelligence algorithms for evaluating sales variety of seeds and period, and the performance of sales in a particular place has also been analyzed. The sample data collected and forecasting timeseries employed Auto-Regressive Integrated Moving Average (ARIMA model). Both the comparisons of Sales Place and Seeds Variety and Sales Period (Seasons) have been carried out for the prediction analysis work. The chapter concluded that forecasting sales values in terms of time helps to identify the required quantity of purchase and leads to avoid the unwanted loss and wastage of seeds. The aim of the present work assisted is to investigate sales trends for a given time period and to build optimal sales model based on season and location. The prediction analysis finds the sales behavior across time period estimated by R-Tool. In this chapter, the work in the quantity of sales prediction for different types of seeds is done based on season and location. The sale on time can prevent ...