Traders and investors rely on time series patterns generated by asset performance to inform and guide their trading and asset allocation decisions. Economists take advantage of analogous patterns in macroeconomic and market data to forecast recessions and other market events.

But you need to be able to spot these patterns in order to use them.

Catch the latest in RiskSpan’s series of machine learning and data workshops as Chirag Soni and Jing Liu, two of RiskSpan’s experts working at the intersection of data science and capital markets, demonstrate how advanced machine learning techniques such as Dynamic Time Warping and KShape can be applied to automate time series analysis and effectively detect patterns hiding in your data.

Chirag and Jing will discuss specific applications, explain popular algorithms, and walk through code examples.

Join us on Wednesday, January 13th! 

RiskSpan Director Steven Sun shares a procedural approach to tackling the difficulties of efficiently extracting structured data from images, scanned documents, and handwritten documents using Google’s latest Document AI Solution. This approach greatly improves:

• Effectiveness and accuracy of extracting data which will be otherwise difficult or impossible, and 
• Automating and streamlining the process of feeding extracted data into a data analytic framework