Time Series Forecasting of Agricultural Products Sale Using Deep Learning

Abstract

Due to the massive production of data, the time series dataset is useful for time-based prediction as it holds time related information. Different forecasting techniques help to predict in the field of the stock market, sales, healthcare, banking, weather etc. The coming out of new companies has increased every year, and they have taken part in the competition with other existing organizations with their products and services. As the competition is growing day by day, sales analysis is a must for every organization to make a strong position in the competitive market. In that case, product and area-wise sales analysis can help a company to attain its aim by accomplishing its target. The success of a business mainly relies on the sales of its product. Predicting the sales may help the company to discern the amount of growth rate. Moreover, this research target is to estimate the production amount for their next manufacture. More precise predictions can give fame and make companies successful. Though it is a challenging task, with the help of machine learning algorithms this issue can be sorted out effectively. In this research, we have analyzed the product selling rate and used Multi Step LSTM for forecasting the sales of the agricultural product for the next month sales