Exponentially Weighted Smoothing Function
An Exponentially Weighted Smoothing Function is a smoothing function whose smoothing factor is an exponential function (in which more proximal observations are given relatively more weight than older observations).
- Context:
- It can be applied by an Exponential Smoothing System (that can solve an exponential smoothing task).
- It can be integrated in an Exponential Smoothing-based Forecasting Algorithm, such as Holt's algorithm.
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- Example(s):
- [math]\displaystyle{ \begin{align} s_0& = x_0\\ s_t & = \alpha x_{t} + (1-\alpha)s_{t-1},\quad t\gt 0 \end{align} }[/math] where [math]\displaystyle{ \alpha }[/math] is the smoothing factor, and [math]\displaystyle{ 0 \lt \alpha \lt 1 }[/math].
- an Exponentially Weighted Moving Average (EWMA) which is an autoregressive integrated moving average (ARIMA) (0,1,1) model with no constant term.[6]
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- Counter-Example(s):
- See: Time-Series Forecasting, Kalman Filter, Smoothing Factor, Timeseries Smoothing, Kolmogorov–Zurbenko Filter, Window Function, Simple Moving Average, Window Functions, Signal Processing, Low-Pass Filter, Noise.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/exponential_smoothing Retrieved:2021-3-1.
- Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned and easily applied procedure for making some determination based on prior assumptions by the user, such as seasonality. Exponential smoothing is often used for analysis of time-series data.
Exponential smoothing is one of many window functions commonly applied to smooth data in signal processing, acting as low-pass filters to remove high-frequency noise. This method is preceded by Poisson's use of recursive exponential window functions in convolutions from the 19th century, as well as Kolmogorov and Zurbenko's use of recursive moving averages from their studies of turbulence in the 1940s.
The raw data sequence is often represented by [math]\displaystyle{ \{x_t\} }[/math] beginning at time [math]\displaystyle{ t = 0 }[/math] , and the output of the exponential smoothing algorithm is commonly written as [math]\displaystyle{ \{s_t\} }[/math] , which may be regarded as a best estimate of what the next value of [math]\displaystyle{ x }[/math] will be. When the sequence of observations begins at time [math]\displaystyle{ t = 0 }[/math] , the simplest form of exponential smoothing is given by the formulas: : [math]\displaystyle{ \begin{align} s_0& = x_0\\ s_t & = \alpha x_{t} + (1-\alpha)s_{t-1},\quad t\gt 0 \end{align} }[/math] where [math]\displaystyle{ \alpha }[/math] is the smoothing factor, and [math]\displaystyle{ 0 \lt \alpha \lt 1 }[/math].
- Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned and easily applied procedure for making some determination based on prior assumptions by the user, such as seasonality. Exponential smoothing is often used for analysis of time-series data.