Introducing matrixprofile-ts, a Python Library for Detecting Patterns and Anomalies in Massive Datasets
Time series data is everywhere. From finance to IT to marketing, many companies produce a myriad of metrics from which they hope to extract valuable insights. Within Target, our team collects hundreds of thousands of time series from across the business and monitors them for anomalous events. At the same time, we’re interested in discovering temporal patterns of behavior. How do milk sales vary throughout the week? Does our Kubernetes cluster follow a common auto-scaling pattern? This poses a significant challenge: Each of our metrics can be vastly different, whether through seasonality, trends or overall level.
A team of Target engineers at our Brooklyn Park, Minnesota, campus recently welcomed 45 students of color from two Minneapolis high schools to get a firsthand look at the technology Target is developing and meet with a diverse team of engineers.
The broad scope of data science is a double-edged sword. Our professional lives span multiple fields, each containing decades of valuable discoveries with new insights added every day. How is a data scientist to stay afloat in this vast knowledge lake? Failure to utilize the right tool or approach can be costly for projects as well as careers. How do we avoid remaining in our comfortable tech bubbles, ultimately risking obsolescence?