Ira Cohen on Challenges and Opportunities in Time Series.
Ira Cohen is co-founder, Chief Data Scientist at Anodot1, a startup that uses time series tools to monitor business data in real time, so organizations can proactively resolve revenue, cost, and customer experience issues before they impact business performance. We recently wrote a well-received post that provided a detailed overview on the state of technologies for collecting, storing, and unlocking time series. In this episode we discuss the many existing challenges faced by teams who deal with time series. These challenges provide excellent opportunities for aspiring entrepreneurs and builders, which is why we wanted to highlight them.
Highlights in the video version:
- Modeling tools for streaming data
Challenges caused by insufficient data; possible role of synthetic data generators
Unlocking the potential of synthetic data tools for time series
Better and more efficient tools are needed for multivariate modeling of time series
Automation, and inching towards AutoML for time series
Scaling time series modeling to thousands/millions of sensors
Are specialized time series databases worth it in the long run? What role will Vector Databases play in time series?
What’s next for Ira and the data science team at Anodot?
- A video version of this conversation is available on our YouTube channel.
- Trends and Opportunities in Time Series
- Federico Garza and Max Mergenthaler Canseco (of Nixtla): A comprehensive suite of open source tools for time series modeling
- Aadyot Bhatnagar (of Merlion): Machine Learning for Time Series Intelligence
- Reza Hosseini and Albert Chen (of Greykite): Building a flexible, intuitive, and fast forecasting library
- Sean Taylor (of Prophet): Changes to the data science role and to data science tools
- Sercan Arik: Neural Models for Tabular Data
- Previous podcast episodes pertaining to time series and forecasting.
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 Ben Lorica is an advisor to Anodot and other startups.
[Image: Transitions by Ben Lorica, using images from Infogram.]