


Today I would like to talk to you about the latest work on large model time series forecasting. From Alibaba Damo Academy, a general time series analysis framework based on adapter is proposed, which can be used in long-term forecasting, short-term forecasting, and zero-shot. Remarkable results have been achieved on 7 time series tasks, including few-shot, anomaly detection, time series classification, and time series filling.
Paper title: One size fits all: Universal time series analysis using pre-trained language models and specially designed adapters
Downloadable link: https://arxiv .org/pdf/2311.14782v1.pdf
1. Background
In the field of time series prediction, one of the difficulties in building large models is the lack of sufficient training data like in the NLP or CV fields. . This article proposes a solution, which is to adapt it to time series based on large-scale models trained in the field of NLP or CV, combined with Adapter technology, to solve various time series problems
Adapters are widely used in fields such as NLP and CV. Especially in recent large model applications, adapters are often used to perform lightweight finetune of large models. The Adapter is a lightweight network. By inserting it into some modules in the large model, then fixing the parameters of the large model, and only updating the parameters of the adapter, you can achieve lightweight large model finetune.
Picture
Next, I will introduce to you how in this work of Alibaba Damo Academy, we use adapter to combine pre-trained NLP and CV models to build a unified time series model.
2. Overall structure
The model proposed in this article is based on the pre-trained language model of Freeze parameters and is implemented by combining 4 types of adapters. The overall model structure is shown in the figure below.
Picture
First, for the input time series, we will use the RevIN method for normalization. This means that we subtract the mean from each time series and divide by the variance. Next, we will use the PatchTST method to split the time series into multiple segments through sliding windows and generate segment embeddings. The processed time series will be input into a pre-trained language model in the NLP field. During the entire training process, the original parameters of the language model will remain unchanged, and we will only update the newly added 4 types of adapter parameters
3. Adapter design
This article introduces four types of Adapters that can be plugged into different locations in large models in the fields of NLP and CV to achieve the goal of adapting time series. These four adapters are time adapter, channel adapter, frequency adapter and exception adapter
Time adapter: Time adapter is an MLP network used to fuse time dimension information. In this paper, we adopt a bottleneck structure to first map high-dimensional information in the time dimension or space dimension to a low-dimensional space, and then map it back to the high-dimensional space. The purpose of this is to avoid the risk of over-fitting in the process of extracting time series relationships
Channel Adaptor: The structure of the channel adapter is similar to the temporal adapter. The difference is that it is performed in the spatial dimension and is used to extract the variables of the multivariate sequence. The relationship between them also uses bottleect;
Picture
Frequency Adapter: The frequency adapter extracts time series information in the frequency domain. This part will The time series is mapped to the frequency domain, MLP is performed in the frequency domain, and then mapped back to the time domain to achieve the extraction of global information in the frequency domain.
Anomaly Adapter: This part mainly implements a new time series anomaly detection method. The attention score matrix is used here. For normal sequences, the attention score matrix exhibits periodic repetition characteristics, while abnormal sequences do not. Therefore, a Gaussian kernel is used as anomaly adapter in this article, and the output result of attention and its calculated KL divergence are used for time series anomaly detection.
Picture
In addition, different data will be affected by each adapter to varying degrees. Therefore, a gated network is used in this article to selectively Using adapter
4 and experimental results
, the effects of 7 time series tasks were compared. The time series unified large model proposed in this article achieved results in each task that exceeded those of various SOTA models in the industry. Effect. Taking the long-term prediction task as an example, the unified model based on GPT2 Adapter performs best
picture
The above is the detailed content of Time series multi-task integrated large-scale model based on Adapter and GPT. For more information, please follow other related articles on the PHP Chinese website!

1 前言在发布DALL·E的15个月后,OpenAI在今年春天带了续作DALL·E 2,以其更加惊艳的效果和丰富的可玩性迅速占领了各大AI社区的头条。近年来,随着生成对抗网络(GAN)、变分自编码器(VAE)、扩散模型(Diffusion models)的出现,深度学习已向世人展现其强大的图像生成能力;加上GPT-3、BERT等NLP模型的成功,人类正逐步打破文本和图像的信息界限。在DALL·E 2中,只需输入简单的文本(prompt),它就可以生成多张1024*1024的高清图像。这些图像甚至

Wav2vec 2.0 [1],HuBERT [2] 和 WavLM [3] 等语音预训练模型,通过在多达上万小时的无标注语音数据(如 Libri-light )上的自监督学习,显著提升了自动语音识别(Automatic Speech Recognition, ASR),语音合成(Text-to-speech, TTS)和语音转换(Voice Conversation,VC)等语音下游任务的性能。然而这些模型都没有公开的中文版本,不便于应用在中文语音研究场景。 WenetSpeech [4] 是

“Making large models smaller”这是很多语言模型研究人员的学术追求,针对大模型昂贵的环境和训练成本,陈丹琦在智源大会青源学术年会上做了题为“Making large models smaller”的特邀报告。报告中重点提及了基于记忆增强的TRIME算法和基于粗细粒度联合剪枝和逐层蒸馏的CofiPruning算法。前者能够在不改变模型结构的基础上兼顾语言模型困惑度和检索速度方面的优势;而后者可以在保证下游任务准确度的同时实现更快的处理速度,具有更小的模型结构。陈丹琦 普

由于复杂的注意力机制和模型设计,大多数现有的视觉 Transformer(ViT)在现实的工业部署场景中不能像卷积神经网络(CNN)那样高效地执行。这就带来了一个问题:视觉神经网络能否像 CNN 一样快速推断并像 ViT 一样强大?近期一些工作试图设计 CNN-Transformer 混合架构来解决这个问题,但这些工作的整体性能远不能令人满意。基于此,来自字节跳动的研究者提出了一种能在现实工业场景中有效部署的下一代视觉 Transformer——Next-ViT。从延迟 / 准确性权衡的角度看,

3月27号,Stability AI的创始人兼首席执行官Emad Mostaque在一条推文中宣布,Stable Diffusion XL 现已可用于公开测试。以下是一些事项:“XL”不是这个新的AI模型的官方名称。一旦发布稳定性AI公司的官方公告,名称将会更改。与先前版本相比,图像质量有所提高与先前版本相比,图像生成速度大大加快。示例图像让我们看看新旧AI模型在结果上的差异。Prompt: Luxury sports car with aerodynamic curves, shot in a

人工智能就是一个「拼财力」的行业,如果没有高性能计算设备,别说开发基础模型,就连微调模型都做不到。但如果只靠拼硬件,单靠当前计算性能的发展速度,迟早有一天无法满足日益膨胀的需求,所以还需要配套的软件来协调统筹计算能力,这时候就需要用到「智能计算」技术。最近,来自之江实验室、中国工程院、国防科技大学、浙江大学等多达十二个国内外研究机构共同发表了一篇论文,首次对智能计算领域进行了全面的调研,涵盖了理论基础、智能与计算的技术融合、重要应用、挑战和未来前景。论文链接:https://spj.scien

译者 | 李睿审校 | 孙淑娟近年来, Transformer 机器学习模型已经成为深度学习和深度神经网络技术进步的主要亮点之一。它主要用于自然语言处理中的高级应用。谷歌正在使用它来增强其搜索引擎结果。OpenAI 使用 Transformer 创建了著名的 GPT-2和 GPT-3模型。自从2017年首次亮相以来,Transformer 架构不断发展并扩展到多种不同的变体,从语言任务扩展到其他领域。它们已被用于时间序列预测。它们是 DeepMind 的蛋白质结构预测模型 AlphaFold

说起2010年南非世界杯的最大网红,一定非「章鱼保罗」莫属!这只位于德国海洋生物中心的神奇章鱼,不仅成功预测了德国队全部七场比赛的结果,还顺利地选出了最终的总冠军西班牙队。不幸的是,保罗已经永远地离开了我们,但它的「遗产」却在人们预测足球比赛结果的尝试中持续存在。在艾伦图灵研究所(The Alan Turing Institute),随着2022年卡塔尔世界杯的持续进行,三位研究员Nick Barlow、Jack Roberts和Ryan Chan决定用一种AI算法预测今年的冠军归属。预测模型图


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Zend Studio 13.0.1
Powerful PHP integrated development environment

SublimeText3 Chinese version
Chinese version, very easy to use

SublimeText3 Linux new version
SublimeText3 Linux latest version

Notepad++7.3.1
Easy-to-use and free code editor

Dreamweaver CS6
Visual web development tools
