Search Results for "python time series analysis"

Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included. Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional...

Starting from GIMMS values as ascii data (each line will be treated as individual time series), the application can smooth the ts - values by applying different algorithm that are based on Fouriertransformation. Separation of seasonal figure as well as the detection of linear trends is possible. Possible breaks in longterm mean can be detected with a change-point analysis using CuSum algorithms. Phenological events such as start-of-season, day-of-max/day-of-min, end-of-season can be determined via threshold-based algorithms or via analysis of max. increase of NDVI during green-up. ...

The JTimeSeries has moved to github Please go to https://github.com/JTimeSeries/jtimeseries The SourceForge copy has not been maintained since Sep 2012 A java library to assist with capturing and storing timeseries data/metrics. Provides facilities to publish timeseries data across a network, a lightweight server to persist series data, and client user interface components for real time visualization

JMulTi is an interactive software designed for univariate and multivariate time series analysis. It has a Java graphical user interface that uses an external engine for statistical computations. It uses the framework JStatCom.

JMulTiR is an econometrics package designed for univariate and multivariate time series analysis. The numerical computations and graphics are done in R (www.r-project.org), the GUI is programmed in Java Swing with the jstatcom framework.

Data Mining in Time Series

XMAS supports a new kind of “sit forward” time series microarray analysis through visual interaction and interoperable operators. Domain knowledge is integrated directly into the system to aid users in their analysis.