Time-Series LLMs, Explained with t0-alpha
Time-Series LLMs, Explained with t0-alpha
t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. Raw series are split into 32-step patches, embedded, processed through causal time-attention and group-attention layers, and decoded into future quantiles rather than a single point forecast.
The post Time-Series LLMs, Explained with t0-alpha appeared first on Towards Data Science.
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