Package: salesforce-merlion
Merlion: A Machine Learning Library for Time SeriesTable of ContentsIntroductionComparison with Related LibrariesInstallationDocumentationGetting StartedAnomaly DetectionForecastingEvaluation and BenchmarkingTechnical Report and Citing MerlionIntroductionMerlion 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, anomaly detection,
and change point 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.Merlion's key features areStandardized and easily extensible data loading & benchmarking for a wide range of forecasting and anomaly
detection datasets. This includes transparent support for custom datasets.A library of diverse models for anomaly detection, forecasting, and change point detection, all
unified under a shared interface. Models include classic statistical methods, tree ensembles, and deep
learning approaches. Advanced users may fully configure each model as desired.AbstractDefaultDetectorandDefaultForecastermodels that are efficient, robustly achieve good performance,
and provide a starting point for new users.AutoML for automated hyperaparameter tuning and model selection.Unified API for using a wide range of models to forecast withexogenous regressors.Practical, industry-inspired post-processing rules for anomaly detectors that make anomaly scores more interpretable,
while also reducing the number of false positives.Easy-to-use ensembles that combine the outputs of multiple models to achieve more robust performance.Flexible evaluation pipelines that simulate the live deployment & re-training of a model in production,
and evaluate performance on both forecasting and anomaly detection.Native support for visualizing model predictions, including with a clickable visual UI.Distributed computationbackendusing PySpark,
which can be used to serve time series applications at industrial scale.Comparison with Related LibrariesThe table below provides a visual overview of how Merlion's key features compare to other libraries for time series
anomaly detection and/or forecasting.MerlionProphetAlibi DetectKatsdartsstatsmodelsnixtlaGluonTSRRCFSTUMPYGreykitepmdarimaUnivariate Forecasting✅✅✅✅✅✅✅✅✅Multivariate Forecasting✅✅✅✅✅✅Univariate Anomaly Detection✅✅✅✅✅✅✅✅✅Multivariate Anomaly Detection✅✅✅✅✅✅Pre Processing✅✅✅✅✅✅✅✅Post Processing✅✅AutoML✅✅✅Ensembles✅✅✅✅Benchmarking✅✅✅✅✅Visualization✅✅✅✅✅The following features are new in Merlion 2.0:MerlionProphetAlibi DetectKatsdartsstatsmodelsnixtlaGluonTSRRCFSTUMPYGreykitepmdarimaExogenous Regressors✅✅✅✅✅✅Change Point Detection✅✅✅✅✅Clickable Visual UI✅Distributed Backend✅✅InstallationMerlion consists of two sub-repos:merlionimplements the library's core time series intelligence features,
andts_datasetsprovides standardized data loaders for multiple time series datasets. These loaders load
time series aspandas.DataFrames with accompanying metadata.You can installmerlionfrom PyPI by callingpip install salesforce-merlion. You may install from source by
cloning this repoand callingpip install Merlion/, orpip install -e Merlion/to install in editable mode.
You may install additional dependencies viapip install salesforce-merlion[all],  or by callingpip install "Merlion/[all]"if installing from source.
Individually, the optional dependencies includedashboardfor a GUI dashboard,sparkfor a distributed computation backend with PySpark, anddeep-learningfor all deep learning models.To install the data loading packagets_datasets, clone this repo and callpip install -e Merlion/ts_datasets/.
This package must be installed in editable mode (i.e. with the-eflag) if you don't want to manually specify the
root directory of every dataset when initializing its data loader.Note the following external dependencies:Some of our forecasting models depend on OpenMP. If usingconda, pleaseconda install -c conda-forge lightgbmbefore installing our package. This will ensure that OpenMP is configured to work with thelightgbmpackage
(one of our dependencies) in yourcondaenvironment. If using Mac, please installHomebrewand callbrew install libompso that the OpenMP libary is available for the model.Some of our anomaly detection models depend on the Java Development Kit (JDK). For Ubuntu, callsudo apt-get install openjdk-11-jdk. For Mac OS, installHomebrewand callbrew tap adoptopenjdk/openjdk && brew install --cask adoptopenjdk11. Also ensure thatjavacan be found
on yourPATH, and that theJAVA_HOMEenvironment variable is set.DocumentationFor example code and an introduction to Merlion, see the Jupyter notebooks inexamples, and the guided walkthroughhere. You may find detailed API documentation (including the
example code)here. Thetechnical reportoutlines Merlion's overall architecture
and presents experimental results on time series anomaly detection & forecasting for both univariate and multivariate
time series.Getting StartedThe easiest way to get started is to use the GUI web-baseddashboard.
This dashboard provides a great way to quickly experiment with many models on your own custom datasets.
To use it, install Merlion with the optionaldashboarddependency (i.e.pip install salesforce-merlion[dashboard]), and callpython -m merlion.dashboardfrom the command line.
You can view the dashboard athttp://localhost:8050.
Below, we show some screenshots of the dashboard for both anomaly detection and forecasting.To help you get started with using Merlion in your own code, we provide below some minimal examples using Merlion
default models for both anomaly detection and forecasting.Anomaly DetectionHere, we show the code to replicate the results from the anomaly detection dashboard above.
We begin by importing Merlion’sTimeSeriesclass and the data loader for the Numenta Anomaly BenchmarkNAB.
We can then divide a specific time series from this dataset into training and testing splits.frommerlion.utilsimportTimeSeriesfromts_datasets.anomalyimportNAB# Data loader returns pandas DataFrames, which we convert to Merlion TimeSeriestime_series,metadata=NAB(subset="realKnownCause")[3]train_data=TimeSeries.from_pd(time_series[metadata.trainval])test_data=TimeSeries.from_pd(time_series[~metadata.trainval])test_labels=TimeSeries.from_pd(metadata.anomaly[~metadata.trainval])We can then initialize and train Merlion’sDefaultDetector, which is an anomaly detection model that
balances performance with efficiency. We also obtain its predictions on the test split.frommerlion.models.defaultsimportDefaultDetectorConfig,DefaultDetectormodel=DefaultDetector(DefaultDetectorConfig())model.train(train_data=train_data)test_pred=model.get_anomaly_label(time_series=test_data)Next, we visualize the model's predictions.frommerlion.plotimportplot_anomsimportmatplotlib.pyplotaspltfig,ax=model.plot_anomaly(time_series=test_data)plot_anoms(ax=ax,anomaly_labels=test_labels)plt.show()Finally, we can quantitatively evaluate the model. The precision and recall come from the fact that the model
fired 3 alarms, with 2 true positives, 1 false negative, and 1 false positive. We also evaluate the mean time
the model took to detect each anomaly that it correctly detected.frommerlion.evaluate.anomalyimportTSADMetricp=TSADMetric.Precision.value(ground_truth=test_labels,predict=test_pred)r=TSADMetric.Recall.value(ground_truth=test_labels,predict=test_pred)f1=TSADMetric.F1.value(ground_truth=test_labels,predict=test_pred)mttd=TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels,predict=test_pred)print(f"Precision:{p:.4f}, Recall:{r:.4f}, F1:{f1:.4f}\n"f"Mean Time To Detect:{mttd}")Precision: 0.6667, Recall: 0.6667, F1: 0.6667
Mean Time To Detect: 1 days 10:22:30ForecastingHere, we show the code to replicate the results from the forecasting dashboard above.
We begin by importing Merlion’sTimeSeriesclass and the data loader for theM4dataset. We can then divide a
specific time series from this dataset into training and testing splits.frommerlion.utilsimportTimeSeriesfromts_datasets.forecastimportM4# Data loader returns pandas DataFrames, which we convert to Merlion TimeSeriestime_series,metadata=M4(subset="Hourly")[0]train_data=TimeSeries.from_pd(time_series[metadata.trainval])test_data=TimeSeries.from_pd(time_series[~metadata.trainval])We can then initialize and train Merlion’sDefaultForecaster, which is an forecasting model that balances
performance with efficiency. We also obtain its predictions on the test split.frommerlion.models.defaultsimportDefaultForecasterConfig,DefaultForecastermodel=DefaultForecaster(DefaultForecasterConfig())model.train(train_data=train_data)test_pred,test_err=model.forecast(time_stamps=test_data.time_stamps)Next, we visualize the model’s predictions.importmatplotlib.pyplotaspltfig,ax=model.plot_forecast(time_series=test_data,plot_forecast_uncertainty=True)plt.show()Finally, we quantitatively evaluate the model. sMAPE measures the error of the prediction on a scale of 0 to 100
(lower is better), while MSIS evaluates the quality of the 95% confidence band on a scale of 0 to 100 (lower is better).# Evaluate the model's predictions quantitativelyfromscipy.statsimportnormfrommerlion.evaluate.forecastimportForecastMetric# Compute the sMAPE of the predictions (0 to 100, smaller is better)smape=ForecastMetric.sMAPE.value(ground_truth=test_data,predict=test_pred)# Compute the MSIS of the model's 95% confidence interval (0 to 100, smaller is better)lb=TimeSeries.from_pd(test_pred.to_pd()+norm.ppf(0.025)*test_err.to_pd().values)ub=TimeSeries.from_pd(test_pred.to_pd()+norm.ppf(0.975)*test_err.to_pd().values)msis=ForecastMetric.MSIS.value(ground_truth=test_data,predict=test_pred,insample=train_data,lb=lb,ub=ub)print(f"sMAPE:{smape:.4f}, MSIS:{msis:.4f}")sMAPE: 4.1944, MSIS: 18.9331Evaluation and BenchmarkingOne of Merlion's key features is an evaluation pipeline that simulates the live deployment
of a model on historical data. This enables you to compare models on the datasets relevant
to them, under the conditions that they may encounter in a production environment. Our
evaluation pipeline proceeds as follows:Train an initial model on recent historical training data (designated as the training split of the time series)At a regular interval (e.g. once per day), retrain the entire model on the most recent data. This can be either the
entire history of the time series, or a more limited window (e.g. 4 weeks).Obtain the model's predictions (anomaly scores or forecasts) for the time series values that occur between
re-trainings. You may customize whether this should be done in batch (predicting all values at once),
streaming (updating the model's internal state after each data point without fully re-training it),
or some intermediate cadence.Compare the model's predictions against the ground truth (labeled anomalies for anomaly detection, or the actual
time series values for forecasting), and report quantitative evaluation metrics.We provide scripts that allow you to use this pipeline to evaluate arbitrary models on arbitrary datasets.
For example, invokingpythonbenchmark_anomaly.py--datasetNAB_realAWSCloudwatch--modelIsolationForest--retrain_freq1dwill evaluate the anomaly detection performance of theIsolationForest(retrained once a day) on the
"realAWSCloudwatch" subset of the NAB dataset.  Similarly, invokingpythonbenchmark_forecast.py--datasetM4_Hourly--modelETSwill evaluate the batch forecasting performance (i.e. no retraining) ofETSon the "Hourly" subset of the M4 dataset.
You can find the results produced by running these scripts in the Experiments section of thetechnical report.Technical Report and Citing MerlionYou can find more details in our technical report:https://arxiv.org/abs/2109.09265If you're using Merlion in your research or applications, please cite using this BibTeX:@article{bhatnagar2021merlion,
      title={Merlion: A Machine Learning Library for Time Series},
      author={Aadyot Bhatnagar and Paul Kassianik and Chenghao Liu and Tian Lan and Wenzhuo Yang
              and Rowan Cassius and Doyen Sahoo and Devansh Arpit and Sri Subramanian and Gerald Woo
              and Amrita Saha and Arun Kumar Jagota and Gokulakrishnan Gopalakrishnan and Manpreet Singh
              and K C Krithika and Sukumar Maddineni and Daeki Cho and Bo Zong and Yingbo Zhou
              and Caiming Xiong and Silvio Savarese and Steven Hoi and Huan Wang},
      year={2021},
      eprint={2109.09265},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}