Maual pythonDownloads Downloads pypi version License python

TSAD

Time Series Analysis for Simulation of Technological Processes

The primary purpose of the TSAD (Python module) is to make life easier for researchers who use ML techniques to solve the following problems:

  • Fault Detection of industrial equipment

  • Improvement of technological processes of industrial equipment

    • Performance boost

    • Cost reduction

    • Quality control and management

Solving Fault Detection problem

In TSAD, the problem of fault detection is reduced to the problem of detecting time series anomalies using a well-known technique:

  • Forecast a multivariate Time Series (TS) one point ahead (Also works for univariate TS)

  • Compute residuals between forecast and true values

  • Apply analysis of residuals and thus find anomalies

image-2

Documentation

https://tsad.readthedocs.io/

Getting Started

Installation through PyPi:

pip install -U tsad

# Import 
import sys
sys.path.insert(1, '../')
from tsad.base.pipeline import Pipeline
from tsad.base.datasets import load_skab
from tsad.pipelines import ResidualAnomalyDetectionTaskSet

# loading data
dataset = load_skab()
targets = dataset.target_names 
data = dataset.frame.drop(columns=targets).droplevel(level=0)

# Fit and predict
pipeline = Pipeline(ResidualAnomalyDetectionTaskSet)
pred = pipeline.fit_predict(data,n_epochs=5)

After that, you can see:

image-1

image-1

More details you can find here

Thoughts

We encourage the community also to provide feedback on the desired functionality.

We plan to implement:

  1. More complex preprocessing of time series, especially in the area of reduction to a single sampling rate (problem of unevenly spaced time series)

  2. Implement other SOTA algorithms

  3. The ability to implement any model in our pipeline by just providing a link to GitHub. It seems to be a handy feature as many researchers need to verify their models with others.

  4. Integration with most forecasting and anomaly detection benchmarks.

Some interesting links:

  1. https://github.com/salesforce/Merlion

  2. https://github.com/fastforwardlabs/deepad

  3. https://github.com/HendrikStrobelt/LSTMVis

  4. https://github.com/TezRomacH/python-package-template

  5. https://github.com/khundman/telemanom

  6. https://github.com/signals-dev/Orion

  7. https://github.com/NetManAIOps/OmniAnomaly

  8. https://github.com/unit8co/darts

  9. https://github.com/tinkoff-ai/etna-ts

  10. https://github.com/yzhao062/pyod

  11. https://www.radiativetransfer.org/misc/typhon/doc/modules.html#datasets How include dataset

  12. https://github.com/AutoViML/Auto_TS

  13. https://nuancesprog.ru/p/15161/

  14. https://www.sktime.org/en/stable/

  15. https://github.com/zalandoresearch/pytorch-ts

  16. https://github.com/qdata/spacetimeformer

  17. https://joaquinamatrodrigo.github.io/skforecast/0.6.0/index.html

Про архитектуру:

https://pypi.org/project/catalyst/

Merlion

Alibi Detect

Kats

pyod

GluonTS

RRCF

STUMPY

Greykite

Prophet

pmdarima

deepad

TSAD

Forecasting (Прогнозирование)

✅

✅

✅

✅

✅

✅

✅

✅

Anomaly Detection (Поиск аномалий)

✅

✅

✅

✅

✅

✅

✅

✅

✅

✅

Metrics (Алгоритмы оценки)

✅

✅

✅

✅

✅

Ensembles (Ансамбли)

✅

✅

✅

✅

Benchmarking (Бенчмарки и датасеты)

✅

✅

✅

✅

Visualization (Визуализация результатов)

✅

✅

✅

✅

✅

✅

Data preprocessing (Предварительная обработка данных)

✅

✅

Automated EDA (Автоматизированный разведочный анализ данных)

✅

Dependencies

  • TODO

Repo structure

  └── repo 
    ├───docs       # documentation
    ├───tutorials   # examples
    ├───tsad       # files of library