diff --git a/README.md b/README.md index fc0bc37..71cf1dc 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,7 @@ # Time-series Outlie Detection System +Logo + + TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exahaustive modules for building machine learning-based outlier detection systems including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules including: data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertises to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and wide-range of corresponding algorithms are provided in TODS. This package is developed by [DATA Lab @ Texas A&M University](https://people.engr.tamu.edu/xiahu/index.html). TODS is featured for: diff --git a/docs/img/tods_logo.svg b/docs/img/tods_logo.svg new file mode 100644 index 0000000..1fe3b63 --- /dev/null +++ b/docs/img/tods_logo.svg @@ -0,0 +1,56 @@ + + + + + + + + + + + + + + + + + + + + + + + +