5. Double Exponential Smoothing (Holt's method) | Business forecasting #demand #forecast

This is the fifth video of the lecture series "Business Forecasting". This video is about the double-exponential smoothing method (aka Holt's method) in time-series forecasting. The smoothing methods such as simple moving average, weighted moving average, and simple exponential smoothing are unable to capture the trend component of the data. The double exponential smoothing is able to resolve this problem. Since the trend is captured, this method can also be used in estimating the forecast more than one period ahead. Both the underlying theory and the calculations using Excel have been incorporated in this video. #exponential #smoothing #demand #forecast #qualitative #quantitative #msexcel #timeseries #trend #level #seasonal #smoothingcoefficient #coefficient #sensitivity #responsive #exponentialmovingaverage #exponentiallyweightedmovingaverage #doubleexponentialsmoothing #Holt's #Winters' @classicquants778 The previous module: Optimization using Excel: https://youtube.com/playlist?list=PLmP64hoCcu5GQhUTuv6Cgr5QE083DY2wQ What is double exponential smoothing used for? Use Double Exponential Smoothing as a general smoothing method and to provide short-term forecasts when your data have a trend and do not have a seasonal component. This procedure calculates dynamic estimates for two components: level and trend. Is double exponential smoothing a time series? Double exponential smoothing can model trend components and level components for univariate times series data. Trends are slopes in the data. This method models dynamic gradients because it updates the trend component for each observation. To model trends, DES includes an additional parameter, beta (β). What is level in double exponential smoothing? Double exponential smoothing employs a level component and a trend component at each period. It uses two weights, or smoothing parameters, to update the components at each period. The double exponential smoothing equations are: L t = α Y t + (1 - α) [L t-1 + T t-1] T t = γ[L t - L t-1] + (1 - γ) T t-1