AI炒股实战，Transformer杀疯了！

本文整理了时间序列的时序预测 / 时序-Transformer / 时序-大模型 / 时序-扩散四大方向的最新论文204篇。

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ICLR2024 

ICML 2023

Learning Deep Time-index Models for Time S深度之眼整理eries Forecasting 

KDD 2023

TSMixer: Lightweight MLP-Mixer Model fo深度之眼整理r Multivariate Time Series Forecasting 

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1.iTransformer: InvertedTransformers Are Effective for Time Series Forecastina

2.Pathformer: Multi- Scale Transformers  With Adaptive Pathways For Time Series Forecasting

3.SCALEFORMER: ITERATIVE MULTI-SCALE REFINING TRANSFORMERS FOR TIME

SERIESFORECASTING

4.InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attentionfor Long

Term Time Series Forecasting

5.ContiFormer: Continuous-Time Tansformer for Irreqular Time Series Modeling

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卷积神经网络方法(4种算法模型)

1.CNN

Recent advances in convolutional neural networks

2.WaveNet-CNN

Conditional time series forecasting with convolutional neural networks

3.Kmeans-CNN

Short-term load forecasting in smart grid: a combined CNN and K-means clustering approach

4.TCN

An empirical evaluation of generic convolutional and recurrent networks for sequence modeling

因篇幅有限 仅展示前4篇

循环神经网络方法(3种算法模型）

1.RNN

Bidirectional recurrent neural networks

2.LSTM(长短期记忆网络)

Long short-term memory

3.GRU(门控循环单元)

Learning phrase representations using RNN encoder- decoder for statistical machine translation

Transformer方法(11种算法模型)

1.Transformer

Attention-based models for speech recognition

2.BERT

BERT: pre-training of deep bidirectional transformers for language understanding

3.AST

Adversarial sparse transformer for time series forecasting

4.Informer

Informer: beyond efficient transformer for long sequence time-series forecasting

因篇幅有限 仅展示部分

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大模型处时间序列

1.基于Promtpt的方法

Leveraging Language Foundation Models for HumanMobility Forecasting

2.将时间序列进行离散化处理

AudioLM: a Language Modeling Approach to Audio Generation

3.时间序列-文本对齐代表论文

Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification

4.引入视觉信息

Leveraging Vision-Language Models for Granul深度之眼整理tar Market Change Prediction

5.大模型工具

Unleashing the Power of  Shared Label Structures for Human Activity Recognition

训练时间序列领域大模型

TimetGPT-1

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预约25日晚20:00时序最新热点解读直播课

针对目前的大热时序，我们请来了顶会审稿人chichi老师，在4月25日晚20：00点，解读时空/时序预测研究现状与近期热点。

本次直播大纲：

- 以GNN为主的时空预测模型 

- 以Transformer为主的时序预测模型 

- LLM在时空/时序预测上的应用

- 针对时序新热点和应用总结

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预约25日晚20:00时序最新热点解读直播课