adaptive exponential smoothing
1adaptive exponential smoothing — A quantitative forecasting technique in which averages derived from historical data are smoothed by a coefficient, which is allowed to fluctuate with time in relation to changes in the demand pattern. The larger the coefficient, the greater the… …
2exponential smoothing — See adaptive exponential smoothing …
3mean absolute deviation — MAD A measure of forecast error, for example when carrying out adaptive exponential smoothing of time series data. MAD is the average forecast error, either positive or negative, calculated as the sum of the absolute value of forecast error for… …
4List of statistics topics — Please add any Wikipedia articles related to statistics that are not already on this list.The Related changes link in the margin of this page (below search) leads to a list of the most recent changes to the articles listed below. To see the most… …
5Distribution mangagement system — SCADA systems have been a part of utility automation for at least 15 years and contributing to the decision making process of the control rooms. However, majority of the existing solutions are closely related to distribution network data… …
6List of numerical analysis topics — This is a list of numerical analysis topics, by Wikipedia page. Contents 1 General 2 Error 3 Elementary and special functions 4 Numerical linear algebra …
7Kalman filter — Roles of the variables in the Kalman filter. (Larger image here) In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kálmán. Its purpose is to use measurements observed over time, containing noise (random variations)… …
8Nonparametric regression — is a form of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. Nonparametric regression requires larger sample sizes than regression based on… …
9Predictive analytics — encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events. Such predictions rarely take the form of absolute statements, and are more likely to be… …
10Kernel density estimation — of 100 normally distributed random numbers using different smoothing bandwidths. In statistics, kernel density estimation is a non parametric way of estimating the probability density function of a random variable. Kernel density estimation is a… …
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