Time series forecasting is the process of analyzing time series data using statistics and modeling to make predictions and inform strategic decision-making. Time series forecasting plays a significant role in a wide range of real-life problems containing temporal components. Time series forecasting is essential in analyzing dynamic and complex systems predictions.

   Incremental learning is the effective learning model for time series forecasting, among other models, as time series forecasting predicts future events through a sequence of time. Incremental learning sequentially learns the new knowledge from new data and preserves the previously gained knowledge. Weighted incremental learning model learns the new knowledge to its weight that adapts the changes in the characteristics of the new data. Weighted incremental learning-based time series forecasting significantly produces superior prediction performance.