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|format=commas gives numbers formatted with commas and true minus signs. For reasons of retrospective compatibility, the default when inputting dates as year, month and day is |format=raw ; however when inputting dates in full it is |format=commas .
Note that all parameters default to the current date, so for example, the second set of parameters can be left out to calculate elapsed time since a past date: {{Age in years, months, weeks and days |month1 = 1 |day1 = 1 |year1 = 1 }} → 2023 years, 11 months, 2 weeks and 6 days
Period is a duration (has an extra day to include the end date). format=commas: Numbers over 999 are formatted with commas. format=raw: Use a hyphen (-) to indicate a negative date difference instead of a minus (−); a hyphen may allow the result to be used in a calculation. sep=comma: Separator between items is a comma: 1 year, 2 months, 3 days.
The temporal mean is the arithmetic mean of a series of values over a time period. Assuming equidistant measuring or sampling times, it can be computed as the sum of the values over a period divided by the number of values. A simple moving average can be considered to be
In statistics, trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis , as described in Trend estimation .
The MAE is conceptually simpler and also easier to interpret than RMSE: it is simply the average absolute vertical or horizontal distance between each point in a scatter plot and the Y=X line. In other words, MAE is the average absolute difference between X and Y.
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.