Time series forecasting is a statistical technique used to predict future values based on historically observed data points ordered by time. Widely used in finance, economics, and business, it helps stakeholders anticipate future trends and make informed decisions. A time series is a sequence of data points, measured typically at…
Triple Exponential Smoothing, commonly known as the Holt-Winters Method, extends upon Double Exponential Smoothing to address time series data that contains both a trend and a seasonal component. It incorporates three equations to capture the level, trend, and seasonality of a dataset, making it particularly useful for predicting values in…
Double Exponential Smoothing, also known as Holt’s Linear Exponential Smoothing, is a time series forecasting method that extends Simple Exponential Smoothing. While Simple Exponential Smoothing is best suited for time series without a trend, Double Exponential Smoothing can handle time series data with a trend but no seasonality. The primary…
Simple Exponential Smoothing (SES) is a time series forecasting method that is especially suitable for univariate data without a trend or seasonal pattern. It uses weighted averages of past observations to forecast future points. The method is ‘exponential’ because the weights decrease exponentially as observations get older. Key Concept: Smoothing…