International Research Journal of Education and Technology

Sales forecasting is a critical component of business operations, as accurate predictions can help enterprises to improve their operations and make more informed decisions. With the development of artificial intelligence (AI), there are increasing methods to solve the forecasting problem. One of these methods is the use of the Long short- term memory (LSTM) model, an artificial recurrent neural network (RNN) architecture used in the field of deep learning. The LSTM model is particularly useful for time-series regression problems, such as sales forecasting, as it can process entire sequences of data rather than just single data points. This means that it can remember information from previous data points in the sequence and use it to inform its predictions for future data points. To optimize the performance of the LSTM model for sales forecasting, the proposed system involves a number of steps. First, the data is subjected to Exploratory Data Analysis (EDA) and graphical visualization techniques to identify patterns and trends in the data. This allows the model to better understand the underlying structure of the data and make more accurate predictions. Once the patterns and trends have been identified, the data is pre- processed to optimize the structure of the input data sets. This might involve data cleaning, normalization, or other techniques to ensure that the data is in a suitable format for the LSTM model. The deep learning approach used in the proposed system is specifically designed for forecasting non-stationary time series data. This type of data involves predicting future values based on previously observed values, where the underlying patterns and trends in the data may change over time. To account for this, the neural network model incorporates time trend correction, which helps to adjust for any underlying trends or patterns in the data that may impact future sales.

accurate and reliable sales forecasts that can help enterprises to improve their operations and make more informed decisions. By combining the power of the LSTM model with EDA, graphical visualization, and data pre-processing techniques, the system is capable of handling complex time-series data and generating accurate predictions that can help businesses stay ahead of the competition.