An Integrated Approach for Time Series Forecasting of High-Demand Haircare Products in Rural and Urban Areas Using Machine Learning and Statistical Techniques.

Abstract

In this paper, we present a method for using machine learning and statistical techniques to forecast high-demand haircare products in rural and urban areas using an integrated model in order to utilize machine learning and statistical techniques. We intend to compare and contrast different forecasting methods in this study. Our goal is to determine which forecasting method consistently produces accurate forecasts, even on smaller datasets. Specifically, we will be utilizing a Fast-Moving Consumer Goods (FMCG) haircare dataset as input data in this research. There are a number of ways to analyze this dataset in order to discover trends and patterns in consumer demand for haircare products in both the rural and urban areas. By analyzing the dataset, trends and patterns can be identified which then can be used to develop a forecasting model that can be used to make future predictions. As part of this study, we will examine many statistical and machine learning techniques that are commonly used in statistical and machine learning research, such as neural networks, regression, ARIMA time series forecasting and support vector machines, among others. The models will be evaluated based on their accuracy, precision, and recall, and the results will be compared across various scenarios and levels of aggregation as part of the evaluation process. As a result of a recent study, it has been found that machine learning techniques, particularly neural networks and support vector machines, perform significantly better than statistical methods in terms of precision and accuracy. In addition to providing valuable insight into the underlying trends and patterns within a data set, statistical methods like ARIMA and regression are more interpretable and offer more in-depth insight, and they offer a deeper understanding of the data set. As part of the study, the aggregation level was also stressed as an important element to consider when constructing forecasting models. As a result of this research, it has been proven that models developed using machine learning techniques typically perform better than those developed with a lower level of aggregation, especially when they are developed with a higher level of aggregation. A significant portion of the study provides valuable insight into the effectiveness of various forecasting techniques for high-demand haircare products in both urban and rural areas, which is clear from its results. As a result of this paper, businesses will be able to make better decisions regarding inventory management and supply chain optimization in the future through the use of an integrated approach to the analysis of FMCG data. This integrated approach can be applied to other FMCG datasets as well.