AI 与机器学习2024arxiv.org
Chronos：学习时间序列的语言
Chronos: Learning the Language of Time Series
进阶 · 约 50 分钟读完前置知识：时间序列预测基础、Transformer 基础、概率预测
导读
Chronos 是亚马逊提出的时序预测基础模型，将时间序列量化、分词后用 Transformer 语言模型架构进行预训练，实现零样本预测。论文探讨了把时序数据当作“语言”来学习的可行性，并在多个基准上与传统统计和机器学习方法对比。
HN 关注点：预训练基础模型能否在时序预测上真正超越传统方法——M 竞赛中 ML 方法历来表现不佳，讨论焦点是 tokenization 的必要性及此类模型在非平稳数据（如金融）上的实用性。
HN 评论精选
这几条评论里：质疑 2 补充 1 亲历 1
质疑质疑将时序数据分词的必要性
也许我漏掉了什么显而易见的东西，但把时间序列量化和分词的意义何在？我们对文本分词是因为文本不是数字。而时间序列本身就是数字，这样做等于把数字变成精度更低的数字？缩放和中心化很平常，所有时序 ML 都会做，但我不明白为什么在这之后还需要 token。
meow_cat查看英文原文
Maybe I'm missing something obvious, but what is the idea behind quantizing and tokenizing time series? We tokenize text because text isn't numbers. In the case of time series, we're... turning numbers into less precise numbers? The benefit of scaling and centering is trivial and i guess all timeseries ML does it, but I don't see why we need a token after that.
补充引用开源对比，指出 ML 方法在 M 竞赛中历来不敌线性模型和树模型
我没有利益相关，但看到有开源对比传统时序策略挺有意思：https://github.com/Nixtla/nixtla/tree/main/experiments/amazon-chronos 。总的来说，M 竞赛（时序预测的奥运会）一直让 ML 方法很受挫……线性模型表现惊人地好，而获胜的 ML 模型通常是基于较老的树方法的变体（LightGBM 是常客）。Transformer 架构能否在这里取得真正进展，值得观察。
izyda查看英文原文
I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazon-chronos In general, the M-Competitions (https://forecasters.org/resources/time-series-data/), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have won, generally seem to be variants of older tree-based methods (ie. LightGBM is a favorite). Will be interesting to see whether the Transformer architecture ends up making real progress here.
质疑从金融视角质疑基础模型在高噪声非平稳数据上的适用性
我来自金融行业，一直好奇这些大型预训练模型能否用于金融时间序列。在存在明显（哪怕很隐蔽）平稳模式的领域（如工业或生物指标），预训练模型很有吸引力。但金融数据信噪比极低、极度非平稳甚至混沌，我很难看出预训练基础模型的用处。
BrokrnAlgorithm查看英文原文
Coming from finance, I always wonder how and if these large pre-trained models are usable on any financial time series. I see the appeal of pre-trained models in areas where there is clearly a stationary pattern, even if its very hidden (i.e industrial or biological metrics). But given the inherently high signal/noise ratio and how extremely non-stationary or chaotic the financial data processes tend to be, i struggle to see the use of pre-trained foundation models.
亲历分享实际需求：用简单指标筛选重要时序图表，认为 Chronos 过重
Chronos 对我想做的事可能大材小用了。我 2024 年继承了一个大型时序 JSON 数据集，用 Observable Framework 写了 Rust 数据加载器来解析并绘制折线图。数据有几百张跨多年的图，我想找出该关注哪些图，思路是对每张图计算波动性、趋势方向、斜率、水平位置等指标。我不需要预测，只想要一个能展示最重要图表的仪表盘，求推荐相关库、AI 或数据库。
hbcondo714查看英文原文
Chronos is probably overkill for what I am looking to do with time series data. I just did an Ask HN on time series[0] but unfortunately didn't get the replies I was hoping for. Maybe this thread can get the bump I need: I inherited a large time series JSON dataset in 2024. I've been successful in using the Observable Framework[1] by writing a Rust (rust-script) data loader[2] to parse and plot simple line charts[3] to visually see the data. There are hundreds of graphs over years of data so I would like to identify what graphs I should be paying attention to. My initial thought is to calculate metrics on each graph such as: - Variability: how "spread out" are the data points from one another? - Trend: direction of data path, up or down? - Slope: are the data points increasing or decreasing? - Level: where are the data points on the vertical axis? What libraries, AI, databases, etc... would you recommend that would allow me to calculate these values? I am no data scientist and don't need forecasting but overall, I just want a dashboard that shows the most "important" graphs. [1] https://observablehq.com/framework/ [2] https://observablehq.com/framework/loaders [3] https://observablehq.com/@observablehq/plot-simple-line-chart edit: the x-axis is Time while the y-axis can be values such as duration, frequency, intervals [0] https://news.ycombinator.com/item?id=39763246
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