Real-Time Detection of Malicious Behavior

Online finical service vendors will most likely face a challenge to detect malicious behaviors quick enough, then disable accounts and/or users associated with the malicious behaviors.

To achieve this goal, they need to collect user behavior data and apply online machine learning based algorithms to classify accounts at risk.

We introduce a simple real time fraud detection system. It implements a naive rule engine based on Flink’s Streaming API. Code can be located here.

Time Series Decomposition for IoT Applications

Today, typical IoT applications collect millions or even billions data records via field-deployed sensors. Those data are time series by nature and may be used for various data driven business intelligence such as analytical insights, predictive monitoring, and prescriptive maintenance.

However, before Data Scientists or Machine Learning Practitioners can apply models on top of these data, one of challenges during pre-processing is that data sources may have been aggregated. Therefore, it is essential to decompose one time series curve into several components.

We show that how to decompose multiple seasonality in time series data with Python and Pandas on Jupyter notebook.