Book Sales Forecasting with Bidirectional LSTM: Outlier Handling and Overfitting Reduction Using Clipping and Early Stopping

This study aims to predict book sales using a Bidirectional Long Short-Term Memory (LSTM) model combined with clipping and early stopping techniques to handle outliers and reduce overfitting. The dataset consists of daily book sales records with temporal and categorical variables. The preprocessing process includes feature engineering, logarithmic transformation, standardization, and clipping on the target variable. The dataset is formed in time-series format with a sliding window approach. The model is evaluated using MSE, MAE, RMSE, and R². The results show that the integration of clipping and early stopping provides optimal prediction performance, with an R² value of 0.87 and an RMSE of 0.44. These findings demonstrate the effectiveness of the Bidirectional LSTM approach in forecasting complex and dynamic book sales. This paper is part of the author’s undergraduate thesis at Universitas Amikom Yogyakarta.