Built a production-ready Transformer-based deep learning model for long-range multivariate time series forecasting on the Electricity Transformer Temperature (ETT) dataset. The model predicts the next 24-hour oil temperature using 96-step historical multivariate inputs, outperforming LSTM and ARIMA baselines in RMSE and MAE.