This tutorial demonstrates how to build an end-to-end forecasting pipeline using TimeCopilot, a tool that integrates various forecasting models. The process involves preparing a dataset with real airline passenger data and a synthetic series containing anomalies. It then evaluates a range of statistical models, including Prophet and Chronos, and optionally GPU-based models like TimesFM, to identify the best performer. The workflow includes generating probabilistic forecasts, visualizing trends, detecting unusual observations, and utilizing an LLM agent for model selection and interpretation. AI
IMPACT Demonstrates practical application of foundation models in time-series forecasting and anomaly detection.
RANK_REASON Tutorial on using a specific software tool (TimeCopilot) with existing models.
- AirPassengers
- AutoARIMA
- AutoETS
- Chronos
- foundation model
- graphics processing unit
- IPython
- NumPy
- Prophet
- SciPy
- SeasonalNaive
- Theta
- TimeCopilot
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
