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In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set. Variations include: simple, cumulative, or weighted forms. Mathematically, a moving average is a type of convolution.
In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [ 1 ] [ 2 ] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.
Exponential smoothing or exponential moving average (EMA) 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 ...
SAP: the APO-FCS package [17] in SAP ERP from SAP allows creation and fitting of ARIMA models using the Box–Jenkins methodology. SQL Server Analysis Services: from Microsoft includes ARIMA as a Data Mining algorithm. Stata includes ARIMA modelling (using its arima command) as of Stata 9. StatSim: includes ARIMA models in the Forecast web app.
SAP Business One is an enterprise resource planning application designed for small and medium-sized enterprises, and marketed by the German company SAP SE. As a company, SAP Business One focuses on automating key business functions in finance, operations, and human resources .
Exponentially weighted moving average (EWMA) is an alternative model in a separate class of exponential smoothing models. As an alternative to GARCH modelling it has some attractive properties such as a greater weight upon more recent observations, but also drawbacks such as an arbitrary decay factor that introduces subjectivity into the ...
The idea is do a regular exponential moving average (EMA) calculation but on a de-lagged data instead of doing it on the regular data. Data is de-lagged by removing the data from "lag" days ago thus removing (or attempting to) the cumulative effect of the moving average.
SAP HANA includes a number of analytic engines for various kinds of data processing. The Business Function Library includes a number of algorithms made available to address common business data processing algorithms such as asset depreciation, rolling forecast and moving average. [39]