Sales Prediction Using LSTM and BiLSTM Models: A Deep Learning Approach for Time Series Forecasting
Accurate sales forecasting significantly impacts planning strategies, inventory control, and business operations. Traditional methods of forecasting are often inadequate in recognizing intricate, multidimensional trends over time in sales data. This paper focuses on evaluating the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks in forecasting sales time series. Due to their sophisticated nature, LSTM and BiLSTM networks are an appropriate choice for sales forecasting since they recognize sequential data patterns and long-range dependencies as well as bidirectional dependencies. This research entails the complete process of sales data from preprocessing and feature engineering to model training using LSTM and BiLSTM networks. Important evaluation metrics such as MAE, RMSE, and MAPE are used to compare the models' performances. Experimental results suggest that BiLSTM significantly outperforms LSTM in recognizing sales fluctuation patterns. This, in turn, generates more accurate forecasts. The results demonstrate the ability of deep learning techniques to improve the precision of sales forecasting and, in turn, assist companies in optimizing resource allocation and increasing profitability. Including factors such as economic indicators, industry benchmarks, and advertising would provide even more value in future model refinement.