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Analyze and predict time series data with the ChaosKit library.
ChaosKit is supplied as a web service, .Net or Java class libraries for storing,
analyzing and predicting time series data using techniques from Chaos Theory.
It contains a temporal database to store time-tagged samples of a single variable and
can perform various well known analyses for chaotic behavior. It can calculate optimal
embedding dimensions and separations, and perform short term iterated predictions on
the data using a memory based modeling algorithm.
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Applications are:
- Analysis and prediction of complex systems with feedback
- Analysis of research data
- Quantitative analysis of financial time series data for short term trading of futures, stocks, bonds and currencies.
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Features are:
- Online evaluation environment
- Support for financial time series - data and predictions can be quantized into standard trading units and trading times/days can be specified.
- Calculates various Chaos Theory measures: Hurst Exponent, Lyapunov Exponent and Fractal Dimension.
- Uses Takens' embedding method to create a model of the attractor of the time series.
- Determines optimal embedding parameters using the techniques of False Nearest Neighbors and Mutual Information.
- Memory-based prediction predicts from nearest historic neighboring points in the embedding space.
- Predictions can be single or multiple iterated values at user defined time intervals.
- An optional "forgetting" parameter ignores data older than a user configurable time.
- The class library includes support for general purpose KD Trees of arbitrary objects.
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