daosword / time-series-forecasting-and-deep-learning Goto Github PK

1. Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
   code: https://github.com/google-research/google-research/tree/master/KNF
2. Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
   code: https://github.com/weifantt/Dish-TS

ClaSP - Time Series Segmentation
https://github.com/ermshaua/time-series-segmentation-benchmark

Voice2Series: Reprogramming Acoustic Models for Time Series Classification
https://github.com/huckiyang/Voice2Series-Reprogramming

CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
https://github.com/floft/calda

tsaug
tsaug is a Python package for time series augmentation.

1. Machine learning for transportation data imputation and prediction
   transdim

2. A Python package to discover stochastic differential equations from time series data
   PyDaddy

3. pyclustering is a Python, C++ data mining library
   pyclustering

Neural SDEs for Conditional Time Series Generation and the Signature Wasserstein-1 metric
https://github.com/pere98diaz/Neural-SDEs-for-Conditional-Time-Series-Generation-and-the-Signature-Wasserstein-1-metric

1. Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting
   Code https://github.com/ddz16/Preformer
2. WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting
   Code https://github.com/choyi0521/WaveBound
3. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
   Code https://github.com/yuqinie98/PatchTST
4. N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
   Code https://github.com/cchallu/n-hits
5. ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
   Code https://github.com/salesforce/ETSformer
6. Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
   Code https://github.com/Scaleformer/Scaleformer

I noticed two repositories summed up with method models. There are some papers and codes for time series. You might be able to add some new articles from here.Awesome-Diffusion-Models and awesome-neural-ode.

1. Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series
   Code https://github.com/duongtrung/time-series-temporal-saliency-patterns
2. First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
   Code https://github.com/xiyuanzh/gluonts/tree/tdformer/src/gluonts/nursery/TDformer
3. Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
   Code https://github.com/slaweks17/ES-adRNN-with-Context

1. TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting
   code https://github.com/OrigamiSL/TCCT2021-Neurocomputing-
2. Area2Area Forecasting: Looser Constraints, Better Predictions [Manuscript submitted to journal Neurocomputing]
   code https://github.com/OrigamiSL/A2A
3. FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting[Manuscript submitted to KBS]
   code https://github.com/OrigamiSL/FDNet
4. Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect
   code https://github.com/OrigamiSL/RTNet2022
5. GBT: Two-stage Transformer Framework for Non-stationary Time Series Forecasting[Manuscript submitted to Information Sciences]
   code https://github.com/OrigamiSL/GBT

1. Handling Concept Drift in Global Time Series Forecasting
   https://github.com/Neal-Liu-Ziyi/Concept_Drift_Handling
2. Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting
   https://github.com/Pieter-Cawood/FFORMA-ESRNN
3. UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning
   https://github.com/LceOmlet/UniTS

Diffusion Models for Time Series Applications: A Survey

https://arxiv.org/abs/2305.00624

TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers
https://github.com/fahim-sikder/TransFusion

If you also find this useful, can you add this feature?
Also, I noticed that you added resources for anomaly detection. Here, I want to share a repository DeepOD under development.
By the way, will submitting a paper on anomaly detection have any conflicts with time series forecasting?

https://github.com/deel-ai/puncc
Puncc is a python library for predictive uncertainty quantification using conformal prediction.

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
https://github.com/dongtsi/DeepAID

Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling
https://github.com/saurf4ng/eTaPR

FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection
https://github.com/jlidw/FluxEV

Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization
https://github.com/eBay/RANSynCoders

tsam
Time series aggregation module (tsam). Determines typical operation periods or dereases the temporal resolution

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

Self-Supervised Contrastive Learning for Long-term Forecasting
https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating

Unable to access https://neuralprophet.com/html/index.html, use https://github.com/ourownstory/neural_prophet to replace.

1. Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
   code https://github.com/DurstewitzLab/mmPLRNN
2. Closed-Form Diffeomorphic Transformations for Time Series Alignment
   code https://github.com/imartinezl/difw
3. Domain Adaptation for Time Series Forecasting via Attention Sharing
   code https://github.com/DMIRLAB-Group/SASA
4. Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
   code https://github.com/hrna-ox/camelot-icml
5. Adaptive Conformal Predictions for Time Series
   code https://github.com/mzaffran/AdaptiveConformalPredictionsTimeSeries
6. Utilizing Expert Features for Contrastive Learning of Time-Series Representations
   code https://github.com/boschresearch/expclr
7. Modeling Irregular Time Series with Continuous Recurrent Units
   code https://github.com/boschresearch/Continuous-Recurrent-Units

1. Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
2. CONTRASTIVE LEARNING FOR UNSUPERVISED
   DOMAIN ADAPTATION OF TIME SERIES

Unsupervised Representation Learning for Time Series: A Review
https://github.com/mqwfrog/ULTS

Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
https://github.com/aurelien-renault/Automatic-Feature-Engineering-for-TSC

DEEPTSF: CODELESS MACHINE LEARNING OPERATIONS FOR TIME SERIES FORECASTING
https://github.com/I-NERGY/DeepTSF

Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach
https://github.com/agustdd/floss

FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
code https://github.com/Zero-coder/FECAM

SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting

1.Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
code https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE
2. Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution
code https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/Conformer

1. Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
   code https://github.com/g-benton/Volt
2. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
   code https://github.com/SYLan2019/DSTAGNN
3. Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
   code
4. CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
   code https://github.com/AdityaLab/CAMul
5. EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
   code https://github.com/sheoyon-jhin/EXIT
6. RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
   code https://github.com/DiMarzioBian/RETE_TheWebConf
7. Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
   code https://github.com/BIRD-TAO/CLCRN
8. TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
   code https://github.com/liu-yushan/TLogic
9. Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
   code https://github.com/ruizhao26/STE-FlowNet
10. ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction
    code https://github.com/k51/STGSP
11. DEPTS: DEEP EXPANSION LEARNING FOR PERIODIC TIME SERIES FORECASTING
    code https://github.com/weifantt/DEPTS
12. REVERSIBLE INSTANCE NORMALIZATION FOR ACCURATE TIME-SERIES FORECASTING AGAINST DISTRIBUTION SHIFT
    code https://github.com/ts-kim/RevIN
13. Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting
    code https://github.com/ostadabbas/DSARF
14. Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series
    code https://github.com/thuwuyinjun/DGM2
15. Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting
    code https://github.com/longyuanli/VSMHN
16. Time-Series Event Prediction with Evolutionary State Graph
    code https://github.com/VachelHU/EvoNet
17. Long Horizon Forecasting With Temporal Point Processes
    code https://github.com/pratham16cse/DualTPP
18. Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
    code https://github.com/Z-GCNETs/Z-GCNETs

1. TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
   https://github.com/Atik-Ahamed/TimeMachine

2. An Analysis of Linear Time Series Forecasting Models

3. Self-Supervised Learning for Time Series: Contrastive or Generative?
   https://github.com/DL4mHealth/SSL_Comparison

Conformal prediction interval for dynamic time-series
https://github.com/hamrel-cxu/EnbPI
Sequential Predictive Conformal Inference
https://github.com/hamrel-cxu/SPCI-code

Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features
https://github.com/sunsunmin/Look_Ahead

1.Anomaly Detection with Score Distribution Discrimination
https://github.com/Minqi824/Overlap. [Temporarily unavailable]

2.Precursor-of-Anomaly Detection for Irregular Time Series
https://github.com/sheoyon-jhin/PAD