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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
statsmodels – Python package for statistics and econometrics (regression, plotting, hypothesis testing, generalized linear model (GLM), time series analysis, autoregressive–moving-average model (ARMA), vector autoregression (VAR), non-parametric statistics, ANOVA) Statistical Lab – R-based and focusing on educational purposes
[[Category:Date-computing templates based on current time]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Date-computing templates based on current time]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
A time series database is a software system that is optimized for storing and serving time series through associated pairs of time(s) and value(s). [1] In some fields, time series may be called profiles, curves, traces or trends. [ 2 ]
Software timekeeping systems vary widely in the resolution of time measurement; some systems may use time units as large as a day, while others may use nanoseconds.For example, for an epoch date of midnight UTC (00:00) on 1 January 1900, and a time unit of a second, the time of the midnight (24:00) between 1 January 1900 and 2 January 1900 is represented by the number 86400, the number of ...
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.
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.
The five-number summary is a set of descriptive statistics that provides information about a dataset. It consists of the five most important sample percentiles: . the sample minimum (smallest observation)