Exploring the Shift to Foundation Models in Time-Series Forecasting
By
tiwarinitish86
Toasted golden, schmeared with insight. Top of the rack.
Summary
The article discusses the shift in time-series forecasting towards new 'foundation' models that leverage large language models (LLMs) instead of traditional statistical methods like ARIMA and SARIMA. It explores the potential of creating a reusable forecasting model that can work across various datasets and domains.
Key quotes
· 3 pulledWhere we once depended solely on classic statistical methods, think ARIMA, SARIMA, and Prophet, new 'foundation' models have emerged.
Can we build a single, reusable forecasting model that works across a variety of datasets and domains, instead of painstakingly training a new model for every scenario?
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