Which model utilizes time-series data for making predictions?

The forecasting model is specifically designed to use time-series data to make predictions about future events or values based on previously observed data points over time. This model utilizes patterns such as trends, seasonality, and cycles within the historical data to generate forecasts for future occurrences. Time-series data is characterized by its chronological order, which is essential for understanding how values change over time and for capturing the temporal dependencies that might influence future outcomes.

In the context of making predictions, forecasting models can involve various statistical techniques, including moving averages, exponential smoothing, and ARIMA (AutoRegressive Integrated Moving Average), all grounded in analyzing past data to inform future expectations. This makes the forecasting model the most suitable option when tasked with predicting future values based on historical time-based data.