Impact AI Foundation Model Series: Democratizing Time Series Data Analysis with Max Mergenthaler Canseco from Nixtla
What if the hidden patterns of time series data could be unlocked to predict the future with remarkable accuracy? In this episode of Impact AI, I sit down with Max Mergenthaler Canseco to discuss democratizing time series data analysis through the development of foundation models. Max is the CEO and co-founder of Nixtla, a company specializing in time series research and deployment, aiming to democratize access to advanced predictive insights across various industries.
In our conversation, we explore the significance of time series data in real-world applications, the evolution of time series forecasting, and the shift away from traditional econometric models to the development of TimeGPT. Learn about the challenges faced in building foundation models for time series and a time series model’s practical applications across industries. Discover the future of time series models, the integration of multimodal data, scaling challenges, and the potential for greater adoption in both small businesses and large enterprises. Max also shares Nixtla’s vision for becoming the go-to solution for time series analysis and offers advice to leaders of AI-powered startups.
Key Points:
- Max's background in philosophy, his transition to machine learning, and his path to Nixtla.
- Why time series data is the “DNA of the world” and its role in businesses and institutions.
- Nixtla's advanced forecasting algorithms, the benefits, and their application to industry.
- Historical overview of time series forecasting and the development of modern approaches.
- Learn about the advantages of foundation models for scalability, speed, and ease of use.
- Uncover the range of datasets used to train Nixtla's foundation models and their sources.
- Similarities and differences between training TimeGPT and large language models (LLMs).
- Hear about the main challenges of building time series foundation models for forecasting.
- How Nixtla ensures the quality of its models and the limitations of conventional benchmarks.
- Explore the gap between benchmark performance and effectiveness in the real world.
- He shares the current and upcoming plans for Nixtla and its TimeGPT foundation model.
- He shares his predictions for the future of time series foundation models.
- Advice for leaders of AI-powered startups and what impact he aims to make with Nixtla.
Quotes:
“Time series are in one aspect, the DNA of the world.” — Max Mergenthaler Canseco
“Time is an essential component to understand a change of course, but also to understand our reality. So, time series is maybe a somewhat technical term for a very familiar aspect of our reality.” — Max Mergenthaler Canseco
“Given that we are all training on massive amounts of data and some of us are not disclosing which datasets we’re using, it’s always a problem for academics to try to benchmark foundation models because there might be leakage.” — Max Mergenthaler Canseco
“That’s an interesting aspect of foundation models in time series, that benchmarking is not as straightforward as one might think.” — Max Mergenthaler Canseco
“I think right now in our field probably benchmarks are not necessarily indicative of how well a model is going to perform in real-world data.” — Max Mergenthaler Canseco
“I think that we’re also going to see some of those intuitions that come from the LLM field translated into the time series field soon.” — Max Mergenthaler Canseco
Links:
Max Mergenthaler Canseco on LinkedIn
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