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

April 10, 2022

Multi-Label Clinical Time-Series Generation via Conditional GAN
Chang Lu, Chandan K. Reddy, Ping Wang, Dong Nie, Yue Ning
Generative Adversarial Network Time Series GAN Training Conditional GAN Synthetic EHR

April 7, 2022

Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert, Lars Schmidt-Thieme
Time Series Time Series Forecasting Multivariate Time Series State of the Art Forecasting Shot Classification Heterogeneous Wireless Shot Forecasting

April 2, 2022

Forestry digital twin with machine learning in Landsat 7 data
Xuetao Jiang, Meiyu Jiang, YuChun Gou, Qian Li, Qingguo Zhou
Machine Learning Raw Data Time Series Digital Twin Remote Sensing Image Landsat 8

April 1, 2022

March 30, 2022

Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
Shahab Aslani, Pavan Alluri, Eyjolfur Gudmundsson, Edward Chandy, John McCabe, Anand Devaraj, Carolyn Horst, Sam M Janes, Rahul Chakkara, Arjun Nair, Daniel C Alexander, SUMMIT consortium, Joseph Jacob
Time Series Nodule Detection Lung Nodule Lung Cancer Screening Cancer Prediction

March 28, 2022

DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert
Generative Model Time Series Dynamic Graph Scalable Generative Model

March 26, 2022

AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning
Chunnan Wang, Xingyu Chen, Chengyue Wu, Hongzhi Wang
Time Series Time Series Forecasting Progressive Pruning Time Series Forecasting Model

March 21, 2022

Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cedric Archambeau
Anomaly Detection Time Series Counterfactual Explanation High Dimensional Time Series Deep Learning Based Anomaly Detection

March 18, 2022

March 17, 2022

Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen
Contrastive Learning Transfer Learning Representation Learning Time Series Contrastive Loss Self Supervised Representation Learning Clinical Time Series

March 15, 2022

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu
Time Series Time Series Forecasting Periodic Phenomenon Periodicity Detection

March 14, 2022

Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series
Christoffer Loeffler, Wei-Cheng Lai, Bjoern Eskofier, Dario Zanca, Lukas Schmidt, Christopher Mutschler
Deep Learning Model Time Series Visual Interpretation

March 11, 2022

Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators
Alex Tong Lin, Adrian S. Wong, Robert Martin, Stanley J. Osher, Daniel Eckhardt
Time Series Dynamical System Handwritten Trajectory Parameter Inference Delay Embeddings

March 10, 2022

TiSAT: Time Series Anomaly Transformer
Keval Doshi, Shatha Abudalou, Yasin Yilmaz
Anomaly Detection Time Series Temporal Feature Transformer Based Anomaly Detection

March 9, 2022

Monitoring Time Series With Missing Values: a Deep Probabilistic Approach
Oshri Barazani, David Tolpin
Time Series Time Series Forecasting Multivariate Time Series Time Series Prediction Missing Value High Dimensional Time Series Deep Probabilistic Model

Time Series

Papers

Multi-Label Clinical Time-Series Generation via Conditional GAN

Few-Shot Forecasting of Time-Series with Heterogeneous Channels

Forestry digital twin with machine learning in Landsat 7 data

When to Classify Events in Open Times Series?

GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER

Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning

DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning

HYDRA: Competing convolutional kernels for fast and accurate time series classification

StretchBEV: Stretching Future Instance Prediction Spatially and Temporally

A Comparative Evaluation of Machine Learning Algorithms for the Prediction of R/C Buildings' Seismic Damage

Diverse Counterfactual Explanations for Anomaly Detection in Time Series

Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series

Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators

TiSAT: Time Series Anomaly Transformer

Monitoring Time Series With Missing Values: a Deep Probabilistic Approach