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Traces is a Python library for analysis of unevenly spaced time series in their unaltered form.; CRAN Task View: Time Series Analysis is a list describing many R (programming language) packages dealing with both unevenly (or irregularly) and evenly spaced time series and many related aspects, including uncertainty.
It can handle virtually any data frequency, including daily, weekly, intra-day, and panel data. RATS has extensive graphics capabilities. It can generate high-resolution time series graphs, high-resolution X-Y scatter plots, dual-scale graphs, and can export graphs to many formats, including PostScript and Windows Metafile.
Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.
Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...
getML community is an open source tool for automated feature engineering on time series and relational data. [23] [24] It is implemented in C/C++ with a Python interface. [24] It has been shown to be at least 60 times faster than tsflex, tsfresh, tsfel, featuretools or kats. [24] tsfresh is a Python library for feature extraction on time series ...
LIMDEP is an econometric and statistical software package with a variety of estimation tools. In addition to the core econometric tools for analysis of cross sections and time series, LIMDEP supports methods for panel data analysis, frontier and efficiency estimation and discrete choice modeling.
A time series is one type of panel data. Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset). A data set may exhibit characteristics of both panel data and time series data.
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.