#AnomalyDetection Free Web App "PERFOMALIST" (v1.0) is online and ready for beta testing

Welcome to PERFOMALIST v1.0 - date-time-stamped data on-line analyser  

Functionality: 

1. VISUALIZATION. Weekly (Monthly in v2+) data profiling to visualize patterns, anomalies and short term seasonality via IT-Control Charts. (v1.0)

2. ANALYSIS. Anomalies and Change Points Detection in date-time stamped data. (v2+)

Method: SPC/MASF based Statistical Exception and Trend Detection (SETDS).

INPUT: CSV file with timestamp data (time series observations of a dynamic object). 

Sample input data can be downloaded from from the Download Input Data Sample

Data granularity: hourly (v1.0); minutely, daily (v2+)

OUTPUT:

- IT-Control Chart (see example below)  (v1.0)

- Data cube with  summarized data (168 rows/weekhours - v1.0)

- List of anomalies and change points (v2+)

Requirement: 

Input data should consist of at least 3 weeks of history as the method requires comparing the last 7 days of data (actual) with at least 2 weeks long learning/reference data set (baseline). 

The size of the history is limited by about 5 years. Unlimited size of input data will be implemented in v.2.

Additional resources:

- How to read IT-Control Chart (on-line article)

- On-line class "Performance Anomaly Detection"

Project contributors:
- Anfisa Trubina