Welcome to RealSeries’s documentation!

RealSeries is a comprehensive out-of-the-box Python toolkit for various tasks, including Anomaly Detection, Granger causality and Forecast with Uncertainty, of dealing with Time Series Datasets.

RealSeries has the following features:

• Unified APIs, detailed documentation, easy-to-follow examples and straightforward visualizations.

• All-levels of models, including simple thresholds, classification-based models, and deep (Bayesian) models.

Warning

RealSeries supports Python 3 ONLY. See here to check why.

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API Demo:

RealSeries uses the sklearn-style API and is as easy as

# train the SR-CNN detector
from realseries.models.sr_2 import SR_CNN
sr_cnn = SR_CNN(model_path)
sr_cnn.fit(train_data)
score = sr_cnn.detect(test_data, test_label)

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Getting Started

• Installation
• Examples
  • IsolationForest Example
  • Pytorch basded Neural Network Example
  • Series and Label Visualize Example
  • Granger causality Example
  • VAE for anomaly detection Example

Documentation

• API Cheetsheet
  • Model
  • Data
  • Visualize
• API Reference
  • All Models
  • Utility Functions
• Time Series Datasets
  • Forecast Datasets
  • Anomaly Detection Datasets
  • Granger causality Datasets

RealSeries 101

• Anomaly Detection
  • Problem Description
  • Models
• Granger causality
  • Problem Description of Granger causality
  • Models of Granger causality
• Forecast with Uncertainty
  • Problem Description
  • Models

Contribution

• Contribution
• How to Contribute

Indices and tables

• Index

• Module Index

• Search Page