Time Series

Time series analysis focuses on understanding and modeling data points collected over time, aiming to extract patterns, make predictions, and gain insights from sequential information. Current research emphasizes developing advanced model architectures, such as transformers and recurrent neural networks (RNNs/LSTMs), to handle increasingly complex, high-dimensional, and non-stationary time series data, often incorporating techniques like attention mechanisms and mixture-of-experts models for improved efficiency and accuracy. This field is crucial for numerous applications across diverse domains, including finance, healthcare, and environmental monitoring, enabling better forecasting, anomaly detection, and decision-making based on temporal data.

Papers

August 30, 2023

Fully Embedded Time-Series Generative Adversarial Networks
Joe Beck, Subhadeep Chakraborty
Generative Adversarial Network Synthetic Data Adversarial Training Time Series Adversarial AutoEncoder

August 26, 2023

DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series
Chaoqun Wang, Yijun Li, Xiangqian Sun, Qi Wu, Dongdong Wang, Zhixiang Huang
Time Series Recurrent Neural Network LSTM Network Standard LSTM Long Term Effect

August 25, 2023

August 24, 2023

A Co-training Approach for Noisy Time Series Learning
Weiqi Zhang, Jianfeng Zhang, Jia Li, Fugee Tsung
Contrastive Learning Time Series Time Series Representation Learning Real World Time Series Input Time Series Time Series Benchmark

August 22, 2023

August 21, 2023

Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series
Jan-Philipp Roche, Oliver Niggemann, Jens Friebe
Time Series Supervised Autoencoder Black Box Model Autoencoder Model Closing Set Missing Covariates New Autodiff

August 19, 2023

A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case
Oluwaseyi Ogunfowora, Homayoun Najjaran
Time Series Transformer Model Transformer Based Framework Useful Life Encoder Decoder Transformer Transformer Encoder Model

August 18, 2023

CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Anomaly Detection Time Series Self Supervised Contrastive Representation

August 17, 2023

Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration
Yang Yu, Han Chen
Time Series Structural Health Monitoring Damage Detection Structural Information Vibration Signal Signal Denoising Structural Vibration

August 16, 2023

TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong
Large Language Model Time Series Critique Ability Model Representation Prototype Quality Tactile Ensemble Skill Transfer

August 15, 2023

August 9, 2023

Sparse Binary Transformers for Multivariate Time Series Modeling
Matt Gorbett, Hossein Shirazi, Indrakshi Ray
Neural Network Deep Learning Time Series Multivariate Time Series Lightweight Model Sparse Transformer Precision Transformer

August 8, 2023

MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting
Sahara Ali, Jianwu Wang
Time Series Sea Ice

August 6, 2023

Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series
Chrysoula Kosma, Giannis Nikolentzos, Michalis Vazirgiannis
Time Series Recurrent Neural Network Multivariate Time Series Irregular Time Series

August 3, 2023

Unsupervised Representation Learning for Time Series: A Review
Qianwen Meng, Hangwei Qian, Yong Liu, Yonghui Xu, Zhiqi Shen, Lizhen Cui
Contrastive Learning Time Series Narrative Review Unsupervised Learning Discriminative Feature Unsupervised Representation Learning Unsupervised Representation

August 2, 2023

Time Series

Papers

Fully Embedded Time-Series Generative Adversarial Networks

DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series

TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting

A Co-training Approach for Noisy Time Series Learning

ReLiCADA -- Reservoir Computing using Linear Cellular Automata Design Algorithm

Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms

Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series

A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case

CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection

Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration

TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms

Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts

Sparse Binary Transformers for Multivariate Time Series Modeling

MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting

Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series

Unsupervised Representation Learning for Time Series: A Review

Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion

Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach