Evolutionary Algorithm

Chronetic begins by extracting temporal facts from the time-series dataset. Using these facts it mutates and breeds combinations in order to find patterns. Patterns which accuractly the time-series dataset reproduce to form more complex and more accurate patterns.

Chronological Patterns

Chronetic is fully compatible with ISO calendar systems and most non-ISO calendar systems that define units of years, months and days, etc. Check ChronoUnit for a full list of units comptabile with ChronoPatterns.

Supports InfluxDB

Chronetic fully supports InfluxDB and can be used to analyze pre-existing datasets. Blah blah blah blah blah filler text Jorge likes Kent from pizza hut blah blah.filler text Jorge likes Kent from pizza hut blah blah.

Highly Scalable

Supports parallel execution of evolutionary steps and several parameters to configure the amount of solutions, the fitness & accuracy thresholds, time limits, and more.

Completely Dynamic

The scale, duration, and size of the time-series data makes no difference to Chronetic. Ranging from nanosecond to millennia precision or a dataset of two elements to a dataset of two billion, Chronetic prospers all the same.

Text Descriptions

Patterns found in the time-series data can be translated into human-readable text with ChronoDescriptor. From simple descriptions to verbose descriptions offering deep insight into the patterns in your time-series data.

Design

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Examples

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Build

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