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

March 11, 2023

March 7, 2023

Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops
Charles K. Assaad, Imad Ez-zejjari, Lei Zan
Time Series Causal Graph Causal Relation Root Cause Level Loop Collective Anomaly Summary Causal Graph

March 6, 2023

Robust Dominant Periodicity Detection for Time Series with Missing Data
Qingsong Wen, Linxiao Yang, Liang Sun
Time Series Missing Data Periodicity Detection Trend Filtering

March 2, 2023

Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series
Sondre Sørbø, Massimiliano Ruocco
Anomaly Detection Time Series Comprehensive Taxonomy Evaluation Metric Metric Library Time Series Anomaly Detection Maze Environment

March 1, 2023

February 28, 2023

Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting
Luoxiao Yang, Xinqi Fan, Zijun Zhang
Computer Vision Time Series Time Series Forecasting Deep Vision Model Deep Framework Binary Image

February 23, 2023

February 22, 2023

Information Theory Inspired Pattern Analysis for Time-series Data
Yushan Huang, Yuchen Zhao, Alexander Capstick, Francesca Palermo, Hamed Haddadi, Payam Barnaghi
Time Series Time Series Data Information Theoretic Pattern Recognition Information Theory

February 20, 2023

February 18, 2023

FrAug: Frequency Domain Augmentation for Time Series Forecasting
Muxi Chen, Zhijian Xu, Ailing Zeng, Qiang Xu
Fine Grained Data Augmentation Time Series Time Series Forecasting

February 17, 2023

February 13, 2023

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
Simone Luetto, Fabrizio Garuti, Enver Sangineto, Lorenzo Forni, Rita Cucchiara
Deep Learning Training Data Transformer Based Loss Function Time Series Transformer Architecture Tabular Data Heterogeneous Tabular Data

February 8, 2023

Time Series

Papers

A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series

Explainable AI for Time Series via Virtual Inspection Layers

Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops

Robust Dominant Periodicity Detection for Time Series with Missing Data

Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series

Time Series as Images: Vision Transformer for Irregularly Sampled Time Series

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting

One Fits All:Power General Time Series Analysis by Pretrained LM

A metric to compare the anatomy variation between image time series

Adaptive Sampling for Probabilistic Forecasting under Distribution Shift

Information Theory Inspired Pattern Analysis for Time-series Data

Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies

CNTS: Cooperative Network for Time Series

FrAug: Frequency Domain Augmentation for Time Series Forecasting

Graphical estimation of multivariate count time series

Forecasting with Deep Learning

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data

ASTRIDE: Adaptive Symbolization for Time Series Databases

Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks