In this webinar, Parth Tiwary (neptune.ai’s Product team) and Kamil Kaczmarek (neptune.ai’s DevRel team) dive into best practices that teams use to keep track of time series forecasting models.
The agenda:
- A quick intro to forecasting with ARIMAX, FBProphet, and LSTM
- Why and what to track in time-series forecasting projects?
- How you can use Neptune for:
- Keeping track of each model instance ‘per time series’ for the same algorithm
- Logging model forecasts and visualizing them in Neptune
- Organizing thousands of runs with custom dashboards and views
- Tips and tricks on using Neptune for time-series forecasting
- Q&A session
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Intro
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What is neptune.ai?
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Time series models and what to track
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Demo
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Q&A
Other useful resources
Play with a live time series example project in the Neptune app (no registration needed).
Explore the Prophet + neptune.ai integration.
More about Time-series Forecasting With Model Types: ARIMAX, FBProphet, LSTM
See also
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See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
Explore more resources:
Content type
Area of interest
Area of interest
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