Time Series Forecasting

Time series forecasting is a task that predicts future values by fitting models to historical timestamp data. This task aims to extract patterns and trends from time series data using statistical and machine learning methods to achieve accurate predictions of future data points. Traditional methods include moving average, exponential smoothing, and ARIMA models, while modern techniques such as Recurrent Neural Networks (RNN), Transformers, and XGBoost are also widely applied. Time series forecasting has significant application value in fields like finance, meteorology, and energy, and model performance is typically evaluated using metrics such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).