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A computational algorithm that relies on repeated random sampling to obtain numerical results. It's often used to assess the impact of risk and uncertainty in prediction and forecasting models.

A technique used to smooth out short-term fluctuations and highlight longer-term trends or cycles by averaging data points from a subset of the total data.

This method extends exponential smoothing to capture seasonality by including a seasonal smoothing factor in addition to the level and trend components of the model.

A model that uses a tree-like graph or model of decisions and their possible consequences. It’s used to create a plan to reach a goal or make predictions by learning decision rules inferred from prior data.

A rule of thumb technique for smoothing time series data using the exponentially decreasing weights over time. Useful when reasonable trend or seasonal patterns are absent.

A statistical method that models the relationship between a dependent variable and one or more independent variables using a linear equation. Commonly used for predicting future values and trends.

A methodology for analyzing sequences of data points collected over time intervals to forecast future values based on historical patterns.

A set of algorithms modeled on the human brain that are designed to recognize patterns and perform forecasting by learning from historical data.

An acronym for AutoRegressive Integrated Moving Average, it combines autoregression, differencing for stationarity, and moving average to predict future points in the time series.