A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context.
Apr 21, 2024
Feb 26, 2024 · Our method, TOTEM, or TOkenized Time Series EMbeddings, proposes a simple tokenizer architecture that embeds time series data from varying ...
This repo is an experimental approach that explores a tokenization method that can efficiently tokenize a time-series, for consumption by a NLP Language ...
Tokenizing time series data and treating it like a language enables a model whose zero-shot performance matches or exceeds that of purpose-built models.
Feb 26, 2024 · Our proposed discrete time series tokenization enables the design of general models across a variety of time series do- mains, tasks, and ...
Following tokenization, a time series is mapped to a sequence of tokens. This sequence is now called a corpus. If the tokens are not abstract states and reflect.
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Take the text sequence as an example, a text sentence will be tokenized as a token sequence, and then the tokens are mapped into embedding vectors with a ...
Use fast tokenizers from Tokenizers Run inference with multilingual models Use model-specific APIs Share a custom model Templates for chat models Trainer ...
Jul 3, 2024 · We propose a transformer architecture for time series forecasting with a focus on time series tokenisation and apply it to a real-world ...
Our proposed discrete time series tokenization enables the design of general models across a variety of time series domains, tasks, and evaluation schemas ...