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Model templating allows for the export and import of model configurations in AutoTS. For domain-specific forecasting tasks this can significantly enhance the model search and optimization process.
Exploring how the use of genetic algorithms and continuous learning principles creates a system that can adapt to changing patterns over time as the dynamics of the forecast source data changes.
Developing a visual intuition for how changing metric weightings used by AutoTS can affect the dynamics of the forecast generated.
By adopting a metric weighting approach, businesses can create more nuanced, flexible, and business-aligned forecasts.
Part 2 of a exploratory series on the TimeGPT model from Nixtla. We explore Transformer models and grow our understanding in how they can be applied to time series forecasting.
Part 1 of an exploratory series on the TimeGPT model from Nixtla. We'll take a look at how the model works and how it changes the way we think about common forecasting problems.
TimeGPT is a transformer model for time series forecasting across fields like retail, electricity, finance, and IoT.
Forecasting enthusiasts & data scientists exploring the art and science of predicting future trends and outcomes.