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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.
LibreOffice Calc is the spreadsheet component of the LibreOffice software package. [5] [6]After forking from OpenOffice.org in 2010, LibreOffice Calc underwent a massive re-work of external reference handling to fix many defects in formula calculations involving external references, and to boost data caching performance, especially when referencing large data ranges.
Smartsheet – Online spreadsheet for project management, interactive Gantt, file sharing, integrated with Google Apps [8] Sourcetable [9] – AI spreadsheet that generates formulas, charts, SQL, and analyzes data. ThinkFree Online Calc – as part of the ThinkFree Office online office suite, using Java
A chart created with data from a Microsoft Excel spreadsheet that only saves the chart. To save the chart and spreadsheet save as .XLS. XLC is not supported in Excel 2007 or in any newer versions of Excel. Dialog .xld: Used in older versions of Excel. Archive .xlk: A backup of an Excel Spreadsheet Add-in (DLL) .xll
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 ...
The formula for a given N-Day period and for a given data series is: [2] [3] = = + (()) = (,) 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.
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The idea of the kernel average smoother is the following. For each data point X 0, choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than to X 0 (the closer to X 0 points get higher weights).
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