trendalation 1.0.2

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trendalation

An anomaly detection package for all sorts of trends & time series data. The library leverages algorithms like procrustes analysis to compare and contrast the general shape & trajectory of different trends. The procrustses analysis technique could be leveraged to get the minimum possible mean squared error between 2 distributions (after mathematical transformations). Using the training dataset, an ideal reference trace is determined for comparision along with a classification threshold from the generated error distribution using procrustes against this reference curve.

Installation

Dependencies

trendalation requires:

• Python (>= 3.8)
• NumPy (>= 1.25.0)
• Scikit-Learn (>=1.3.0)
• SciPy (>= 1.11.1)

Using pip

The easiest way to install trendalation is using pip: pip install trendalation

Documentation

Usage

To train a classifier on a collection of traces, a classifier can be trained on the trace distribution. The model sets up the necessary classification criterion by analyzing various population statistics and trends. eg-

from trendalation.classification import ProcClassifier
clf = ProcClassifier()
clf.fit()

To futher optimize the process to the classification criterion determination, additional (optional) training parameters can be provided-

• y: The target values (binary class labels) as integers with 1 representing an anomaly. If provided, classifier is fitted only on "good" samples in an attempt to get higher errors with "bad" samples against the reference curve.
• threshold: Provide a percentile value to determine the threshold value on the procrustes error distribution.
• normalize: Option to normalize traces with respect to themselves before fitting. Useful when traces have highly variables ranges. For example, stock prices.
• ci_width: Confidence interval width parameter. Useful for approximating the location of the divergence or the anomaly on a trace.

The procrustes and trace normalizing functionality can be directly imported from the metric module-

from trendalation.metrics import procrustes
trace1_transformed, trace2_transformed, disparity = procrustes(trace1, trace2)

Help and Support

In order to report bugfixes and new feature requests, simply create a new issue on the repository. The issues will be reviewed by the authors on a regular basis & you're welcome to work on any open issues.

Contribution

This project is a community effort, and everyone is welcome to contribute. Feel free to work on any open issues and setup PRs.