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

May 19, 2022

Inferring extended summary causal graphs from observational time series
Charles K. Assaad, Emilie Devijver, Eric Gaussier
Time Series Causal Discovery Causal Graph Causal Entropy Information Theoretic Measure Summary Causal Graph

May 18, 2022

Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots
Justin Hellermann, Qinzhuan Qian, Ankit Shah
Generative Model Generative Adversarial Network Data Augmentation Time Series Regularization Model Augmented Data

May 13, 2022

Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks
Germán García-Jara, Pavlos Protopapas, Pablo A. Estévez
Generative Model Generative Adversarial Network Data Augmentation Time Series Light Curve

May 10, 2022

Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs
Kelum Gajamannage, Yonggi Park
Time Series Long Short Term Memory Financial Time Series Financial Market Real Time Prediction

May 8, 2022

Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments
Shuhao Yan, Francesca Parise, Eilyan Bitar
Time Series Time Varying Distribution Free Chance Constraint Chance Constrained Data Driven Approximation Nonstationary Environment Data Generating

April 29, 2022

April 28, 2022

Transformers in Time-series Analysis: A Tutorial
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ghulam Rasool, Ravi P. Ramachandran
Transformer Megatron Decepticons Transformer Based Time Series Transformer Architecture Tutorial Review Time Series Analysis

April 27, 2022

Open challenges for Machine Learning based Early Decision-Making research
Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, Joao Gama, Georges Hébrail, Vincent Lemaire, Pierre-François Marteau
Machine Learning Time Series Open Challenge Early Decision Faster Decision

April 26, 2022

Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network
Yash Sharma, Nick Coronato, Donald E. Brown
Convolutional Neural Network Classification Code Time Series

April 23, 2022

Dimension Reduction for time series with Variational AutoEncoders
William Todo, Beatrice Laurent, Jean-Michel Loubes, Merwann Selmani
Time Series Variational Autoencoders Dimensionality Reduction Dimension Reduction Multivariate Time Series Data

April 22, 2022

Causal Analysis of Generic Time Series Data Applied for Market Prediction
Anton Kolonin, Ali Raheman, Mukul Vishwas, Ikram Ansari, Juan Pinzon, Alice Ho
Time Series Causal Analysis Causal Knowledge Market Data Stock Market Prediction Market Prediction

April 18, 2022

Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
Quan Ding, Shenghua Liu, Bin Zhou, Huawei Shen, Xueqi Cheng
Time Series Adversarial Regularization Industrial Monitoring Reconstruction Based Anomaly Detection Robust Forecasting

April 14, 2022

April 13, 2022

Time Series

Papers

Inferring extended summary causal graphs from observational time series

Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots

Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs

Data-Driven Approximations of Chance Constrained Programs in Nonstationary Environments

Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics

Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation

Transformers in Time-series Analysis: A Tutorial

Open challenges for Machine Learning based Early Decision-Making research

Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network

Dimension Reduction for time series with Variational AutoEncoders

Causal Analysis of Generic Time Series Data Applied for Market Prediction

Learning Sequential Latent Variable Models from Multimodal Time Series Data

STD: A Seasonal-Trend-Dispersion Decomposition of Time Series

A data filling methodology for time series based on CNN and (Bi)LSTM neural networks

Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors

Stream-based Active Learning with Verification Latency in Non-stationary Environments

Time Series of Non-Additive Metrics: Identification and Interpretation of Contributing Factors of Variance by Linear Decomposition

Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey

A quantum generative model for multi-dimensional time series using Hamiltonian learning