Time Series Forecasting with Lag Llama

Summary: The video discusses using the Lag-Llama model, an open-source foundation model, to predict overnight low temperatures in New York, helping the speaker decide when to bring their new orange mum plant indoors to protect it from frost.

Keypoints:

• The speaker purchased an orange mum plant and wants to protect it from freezing temperatures in New York.
• Hourly temperature data in New York over the past weeks is collected for analysis.
• The Lag-Llama model, an open-source foundation model, is chosen for predicting overnight low temperatures.
• The setup involves cloning the GitHub repo, installing pre-trained model weights from Hugging Face, and preparing the environment using IBM watsonx.ai studio or other platforms.
• GleonTS is used for working with time series data and forecasting models.
• The dataset for temperatures is sourced from ACS Web services, with missing values filled through interpolation.
• Unlike traditional models like ARIMA, Lag-Llama does not require pre-training on data to forecast temperatures.
• Key model parameters include prediction length (eight hours for overnight forecasts) and context length (one week of historical data for lagged correlations).
• The forecasting process involves creating a lag estimator and a Lag-Llama predictor to generate predictions.
• Forecasts are evaluated against actual temperature data using metrics such as mean absolute percentage error (MAPE).
• The model’s forecasts are visualized with prediction intervals to indicate certainty at each forecast step.
• The speaker decides to bring the plant inside whenever the 50% prediction interval suggests a risk of frost.
• Overall, Lag-Llama performs well, providing a useful tool for time series forecasting in a practical gardening scenario.
• The application of foundation models for time series forecasting is an evolving area with potential benefits.