Prediction of stock price movements is an interesting and important topic in financial market analysis because the ability to understand and predict price trends can provide strategic advantages for investors. This study aims to answer whether the addition of company fundamental features such as Price-to-Earnings (PE), Price-to-Book Value (PBV), and Debt-to-Equity Ratio (DER) can provide improved results compared to using only historical data in the form of closing stock prices (Close) in predicting weekly stock price movements. The data used in this study were taken from the Indonesia Stock Exchange (IDX), focusing on the closing price (Close) as the main feature. To support the analysis, the Close feature is also equipped with ARMA (Autoregressive Moving Average) and ARIMA (Autoregressive Integrated Moving Average) models to capture temporal patterns in historical data. The method used in this research is Temporal Convolutional Network (TCN), with the classification of stock prices into three categories, na
