Ryan Satria Pratama

Comparative Evaluation of ARIMA, LSTM, and Temporal Fusion Transformer for Daily Revenue Forecasting

Accurate revenue forecasting is an important challenge for retail businesses due to fluctuating consumer demand and changing sales patterns. This study evaluates and compares three time series forecasting approaches, namely Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Temporal Fu...