Quant-Reading-List

Papers for AI + quantitative investment

Financial time-series forecasting models

[Arxiv2021] HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

[KDD2021] Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

[WWW2021] REST: Relational Event-driven Stock Trend Forecasting

[ICML2021] Temporally Correlated Task Scheduling for Sequence Learning

[CIKM2021] AdaRNN: Adaptive Learning and Forecasting for Time Series

[CIKM2021] Stock Trend Prediction with Multi-Granularity Data: A Contrastive Learning Approach with Adaptive Fusion

[SIGIR2020] Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments

[KDD2017] Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

[IJCAI2017] A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

Online learning / Incremental learning

[KDD2023] DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

[ICML2022] Online Continual Learning through Mutual Information Maximization

[NeurIPS2021] An Information-theoretic Approach to Distribution Shifts

[ICML2022] Domain Adaptation for Time Series Forecasting via Attention Sharing

[AAAI2022] DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

[AAAI2021] Time Series Domain Adaptation via Sparse Associative Structure Alignment

Ensemble learning

[ICDM2020] DoubleEnsemble A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis

XAI

XAI for graph data

[ICLR2022] Discovering invariant rationales for graph neural networks

[CVPR2022] A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks

[KDD2022] Causal Attention for Interpretable and Generalizable Graph Classification

[ICML2022] Let invariant rationale discovery inspire graph contrastive learning

[ICML2022] Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

[ICLR2021] Graph information bottleneck for subgraph recognition

[AAAI2022] ProtGNN: Towards Self-Explaining Graph Neural Networks

[AAAI2022] Prototype-Based Explanations for Graph Neural Networks

[NIPS2022] CLEAR: Generative Counterfactual Explanations on Graphs

[NIPS2021] Robust Counterfactual Explanations on Graph Neural Networks

[ICLR2022] Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs

[CIKM2021] Towards Self-Explainable Graph Neural Network

Awesome-graph-explainability-papers

Causal Interpretability

XAI for time series data

[NIPS2020]What went wrong and when? Instance-wise feature importance for time-series black-box models

[NIPS2020]Benchmarking Deep Learning Interpretability in Time Series Predictions

[KDD2021] TimeSHAP: Explaining Recurrent Models through Sequence Perturbations

GNN for Multivariate Time-Series

[KDD2022] Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

[KDD2022] Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting

[IJCAI2022] Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting

[AAAI2022] Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting

[AAAI2022] CATN: Cross Attentive Tree-aware Network for Multivariate Time Series Forecasting

[ArXiv2022] Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

[KDD2021] Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting

[KDD2021] Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts

[ICLR2021] Discrete Graph Structure Learning for Forecasting Multiple Time Series

[IJCAI2021] Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks

[NIPS2020] Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Misc

[IJCAI2021] Automated Machine Learning on Graphs: A Survey

[ArXiv2022Mar] Woods: Benchmarks for Out-of-distribution Generalization in Time Series Tasks [Code]

[KDD2020] Dynamic Knowledge Graph based Multi-Event Forecasting

[ICLR2022] COST: Contrastive learning of disentangled seasonal trend representataions for time series forecasting

[WWW2021] New Benchmarks for Learning on Non-Homophilous Graphs

[AAAI2022] Event-Aware Multimodal Mobility Nowcasting

[ArXiv2022July] Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

[WSDM2022] Structure Meets Sequences: Predicting Network of Co-evolving Sequences

[CIKM2022] Retrieval Based Time Series Forecasting

[ArXiv2022July] Less Is More: Fast Multivariate Time Series Forecasting withLight Sampling-oriented MLP Structures

[VLDB2022] METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting

[KDD2021] A Transformer-based Framework for Multivariate Time Series Representation Learning

Survey papers

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