Merlion
v1.0.1
Contents:
merlion: Time Series Intelligence
ts_datasets: Easy Data Loading
Tutorials & Example Code
Basics
Merlion’s Data Format
Anomaly Detection
A Gentle Introduction to Anomaly Detection in Merlion
How to Use Anomaly Detectors in Merlion
Multivariate Time Series Anomaly Detection
Adding New Anomaly Detection Models
Forecasting
A Gentle Introduction to Forecasting in Merlion
How to Use Forecasters in Merlion
Multivariate Time Series Forecasting
Adding a New Forecasting Model
Advanced Features
Tutorial for AutoSARIMA Forecasting Model
Tutorial for Mixture of Expert (MoE) Forecasting Model
Proof of Concept: Inverse Transforms for Forecasters
Merlion
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Tutorials & Example Code
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Tutorials & Example Code
Basics
Merlion’s Data Format
UnivariateTimeSeries: The Basic Building Block
TimeSeries: Merlion’s Standard Data Class
Time Series Indexing & Alignment
TimeSeries: A Few Useful Features
Anomaly Detection
A Gentle Introduction to Anomaly Detection in Merlion
How to Use Anomaly Detectors in Merlion
Model Initialization
Model Training
Model Inference
Quantitative Evaluation
Model Visualization
Saving & Loading Models
Simulating Live Model Deployment
Multivariate Time Series Anomaly Detection
Model Initialization and Training
Model Inference and Quantitative Evaluation
Adding New Anomaly Detection Models
Model Config Class
Model Class
Running the Model: A Simple Example
Visualization
Customizing the Post-Rule
Quantitative Evaluation
Forecasting
A Gentle Introduction to Forecasting in Merlion
How to Use Forecasters in Merlion
Model Initialization
Model Training
Model Inference
Model Visualization and Quantitative Evaluation
Saving & Loading Models
Simulating Live Model Deployment
Multivariate Time Series Forecasting
Model Initialization and Training
Model Inference and Quantitative Evaluation
Adding a New Forecasting Model
Model Config Class
Model Class
Running the Model: A Simple Example
Visualization
Quantitative Evaluation
Defining a Forecaster-Based Anomaly Detector
Advanced Features
Tutorial for AutoSARIMA Forecasting Model
Prepare dataset
Train a full AutoSarima model with approximation (suggested)
Train a full AutoSarima model without approximation
Train a partial autosarima model
Train without enforcing stationarity and invertibility (default)
Tutorial for Mixture of Expert (MoE) Forecasting Model
Load dataset
Create MoE model composed of external expert models and train
Create MoE model containing free parameters (no external experts) and train
Proof of Concept: Inverse Transforms for Forecasters
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