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 …

In smoothing, the data points of a signal are modified so individual points higher than the adjacent points (presumably because of noise) are reduced, and points that are lower than the adjacent …

The name suggests this is achieved by applying a double exponential smoothing which is not the case. The name double comes from the fact that the value of an EMA (Exponential Moving …

When the simple moving median above is central, the smoothing is identical to the median filter which has applications in, for example, image signal processing. The Moving Median is a more …

Curve fitting[1][2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [4][5] Curve fitting can …

The Triple Exponential Moving Average (TEMA) is a technical indicator in technical analysis that attempts to remove the inherent lag associated with moving averages by placing more weight …

TRIX is a triple smoothed exponential moving average used in technical analysis to follow trends. Positive TRIX values indicate bullish price trends, while negative TRIX values indicate bearish …

The default Expert Modeler feature evaluates a range of seasonal and non-seasonal autoregressive (p), integrated (d), and moving average (q) settings and seven exponential …

He is well known for his contributions (and for the contributions of his student, Peter Winters) to exponential smoothing. [1] Holt held BS and MS degrees from the Massachusetts Institute of …

The weight is defined by the kernel, such that closer points are given higher weights. The estimated function is smooth, and the level of smoothness is set by a single parameter. Kernel …