Time Series Forecasting

Time series forecasting involves using historical data points collected or recorded at specific time intervals to predict future values. It is widely applied in various fields such as finance, economics, and environmental science. Methods for time series forecasting can include statistical techniques like ARIMA (AutoRegressive Integrated Moving Average) and machine learning approaches such as recurrent neural networks (RNNs). Accurate forecasting can help organizations make informed decisions and plan for future trends.