182 papers with code • 14 benchmarks • 13 datasets. Time series forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. Time series forecasting has been widely employed in organizational activities. With forecasting techniques, a business can make predictions and provide background information for decision-making (Moore et al., 2018). ... In the business and financial world, we usually measure seasonal component in quarters. (4) Residual effect (R): Residual.