Unverified50% confidenceFactExact time
Amazon's Chronos discretizes continuous numeric series into tokens by quantile binning, then reuses the language model's Transformer architecture for forecasting.
1
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50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
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UnverifiedThe Transformer architecture generates text in an autoregressive manner, outputting one token at a time and appending it to the input sequence to predict the next token, resulting in a serial inference process68% similarUnverifiedIn large language model inference, the Transformer's self-attention mechanism needs to store Key and Value vectors for each processed token, and this batch of storage is the KV Cache.66% similarUnverifiedTransformer模型只能处理数值张量，需要分词器将文本转换为整数ID序列后通过嵌入层映射为连续向量64% similarUnverifiedCurrent mainstream large language models generally adopt the Transformer architecture, whose self-attention mechanism has a computational complexity that grows quadratically with sequence length (O(n²)) when processing long contexts.61% similarUnverifiedKronos采用「量化分词器 + 自回归Transformer」的两阶段技术架构，将OHLCV数据视为语言序列并离散化为token61% similar
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