HYBRID ARIMA AND LSTM DEEP LEARNING MODELS EMPOWERING AND ENHANCING FORECAST ACCURACY IN SALES

Abstract

The business needs to properly forecast the sales that can help in improving inventory management and in future and visionary planning. There is a classical forecasting technique, like the ARIMA (Autoregressive Integrated Moving Average (ARIMA)) model, which is effectively applied in forecasting the linear tendencies of the data, as well as the seasonality (periodic fluctuation). Nevertheless, it may have challenges in dealing with non-linear, complicated, unsteady and uneven data that define the current sales environment. Contrarily, deep learning networks such as the Long Short-term Memory (LSTM) network are very powerful in learning and predicting non-linear, long-term dependencies in data. The absence of a globally optimal model is a major gap within the existing literature with each model having its niche and operating within a specific type of data. The proposed and tested hybrid model of ARIMA and LSTM deep learning that enhances the accuracy of sales forecasting is the proposed research choice. The hybrid model is proposed to use the merits of the two methodologies whereby the ARIMA will deal with the linear bits of the time series, and LSTM models will deal with the non-linear bits.. With the two of these potent methods, we are hoping that a more consistent and adaptable forecasting solution can be achieved. Performance of the model will be compared to standalone ARIMA and LSTM models by different metrics, and the hypothesis will be that the hybrid model will make much lower forecast errors. To optimize business activities and minimize financial risk, the accuracy of the forecasting should be enhanced, and this project provides a distinctive and highly effective strategy in that regard.