Displaying 1 to 20 from 42 results

AnomalyDetection - Anomaly Detection with R

  •    R

AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. The AnomalyDetection package can be used in wide variety of contexts. For example, detecting anomalies in system metrics after a new software release, user engagement post an A/B test, or for problems in econometrics, financial engineering, political and social sciences.

stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis

  •    Python

Whether you are an academic, data scientist, software developer, or time series enthusiast, STUMPY is straightforward to install and our goal is to allow you to get to your time series insights faster. See documentation for more information. Please see our API documentation for a complete list of available functions and see our informative tutorials for more comprehensive example use cases. Below, you will find code snippets that quickly demonstrate how to use STUMPY.

Merlion - Merlion: A Machine Learning Framework for Time Series Intelligence

  •    Python

Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting and anomaly detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. The table below provides a visual overview of how Merlion's key features compare to other libraries for time series anomaly detection and/or forecasting.

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

  •    Python

Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

Chaos Genius - ML powered analytics engine for outlier detection and root cause analysis

  •    Python

Chaos Genius is an open source ML powered analytics engine for outlier detection and root cause analysis. Chaos Genius can be used to monitor and analyse high dimensionality business, data and system metrics at scale. Using Chaos Genius, users can segment large datasets by key performance metrics (e.g. Daily Active Users, Cloud Costs, Failure Rates) and important dimensions (e.g., countryID, DeviceID, ProductID, DayofWeek) across which they want to monitor and analyse the key metrics.

wdbgark - WinDBG Anti-RootKit Extension

  •    C++

WDBGARK is an extension (dynamic library) for the Microsoft Debugging Tools for Windows. It main purpose is to view and analyze anomalies in Windows kernel using kernel debugger. It is possible to view various system callbacks, system tables, object types and so on. For more user-friendly view extension uses DML. For the most of commands kernel-mode connection is required. Feel free to use extension with live kernel-mode debugging or with kernel-mode crash dump analysis (some commands will not work). Public symbols are required, so use them, force to reload them, ignore checksum problems, prepare them before analysis and you'll be happy. Windows BETA/RC is supported by design, but read a few notes. First, i don't care about checked builds. Second, i don't care if you don't have symbols (public or private). IA64/ARM is unsupported (and will not).

luminol - Anomaly Detection and Correlation library

  •    Python

Luminol is configurable in a sense that you can choose which specific algorithm you want to use for anomaly detection or correlation. In addition, the library does not rely on any predefined threshold on the values of a time series. Instead, it assigns each data point an anomaly score and identifies anomalies using the scores.Investigating the possible ways to automate root cause analysis is one of the main reasons we developed this library and it will be a fundamental part of the future work.

Visual-Feature-Attribution-Using-Wasserstein-GANs-Pytorch - Implementation of Visual Feature Attribution using Wasserstein GANs (https://arxiv

  •    Python

This repository contains the code to reproduce results for the paper cited above, where the authors presents a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN). The code works for both synthetic (2D) and real 3D neuroimaging data, you can check below for a brief description of the two datasets. Here is an example of what the generator/mapper network should produce: ctrl-click on the below image to open the gifv in a new tab (one frame every 50 iterations, left: input, right: anomaly map for synthetic data at iteration 50 * (its + 1)).

traffic - A quick and dirty vehicle speed detector using video + anomaly detection

  •    Clojure

A quick and dirty system for tracking vehicle speeds using video + anomaly detection. This project isn't built with generality in mind, but it's open-sourced for the curious. See all the details in the blog post.

skyline - Anomaly detection suite

  •    Python

Skyline is a near real time anomaly detection system, built to enable passive monitoring of hundreds of thousands of metrics, without the need to configure a model/thresholds for each one, as you might do with Nagios. It is designed to be used wherever there are a large quantity of high-resolution time series which need constant monitoring. Once a metrics stream is set up (from StatsD or Graphite or another source), additional metrics are automatically added to Skyline for analysis. Skyline's easily extended algorithms attempt to automatically detect what it means for each metric to be anomalous. The documentation for your version is also viewable in a clone locally in your browser at file://<PATH_TO_YOUR_CLONE>/docs/_build/html/index.html and via the the Skyline Webapp frontend.

TagAnomaly - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)


Anomaly detection labeling tool, specifically for multiple time series (one time series per category). This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

long-range-extrapolation - Capturing Structure Implicitly from Noisy Time-Series having Limited Data

  •    Jupyter

Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a major challenge when estimating time-series models that are robust to overfitting and have well-calibrated uncertainty estimates. Most of the current literature in these fields involve visualizing the time-series for noticeable structure and hard coding them into pre-specified parametric functions. This approach is associated with two limitations. First, given that such trends may not be easily noticeable in small data, it is difficult to explicitly incorporate expressive structure into the models during formulation. Second, it is difficult to know a priori the most appropriate functional form to use. To address these limitations, a nonparametric Bayesian approach was proposed to implicitly capture hidden structure from time series having limited data. The proposed model, a Gaussian process with a spectral mixture kernel, precludes the need to pre-specify a functional form and hard code trends, is robust to overfitting and has well-calibrated uncertainty estimates. Bayesian modeling was adopted to account for uncertainty. Citation for the corresponding paper is as follows.

anomalize - Tidy anomaly detection

  •    R

anomalize enables a tidy workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it’s quite simple to decompose time series, detect anomalies, and create bands separating the “normal” data from the anomalous data. Load the tidyverse and anomalize packages.

go-matrixprofile - golang library for computing matrix profiles along with other time series analysis features

  •    Go

Golang library for computing a matrix profiles and matrix profile indexes. Features also include time series discords, time series segmentation, and motif discovery after computing the matrix profile. Visit The UCR Matrix Profile Page for more details into matrix profiles. Going through a completely synthetic scenario, we'll cover what features to look for in a matrix profile, and what the additional Discords, TopKMotifs, and Segment tell us. We'll first be generating a fake signal that is composed of sine waves, noise, and sawtooth waves. We then run STOMP on the signal to calculte the matrix profile and matrix profile indexes.

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