Time Series Forecasting

Time series forecasting aims to predict future values based on historical data, crucial for diverse applications from finance to healthcare. Current research emphasizes improving model accuracy and efficiency, focusing on transformer-based architectures, state-space models like Mamba, and hybrid approaches combining their strengths, as well as exploring data augmentation and explainable AI techniques. These advancements are driving improvements in forecasting accuracy and interpretability, leading to better decision-making across various sectors and contributing to a deeper understanding of complex temporal dynamics.

Papers

June 29, 2023

Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting
Ngoc-Dung Do, Truong Son Hy, Duy Khuong Nguyen
Graph Neural Network Time Series Forecasting Multivariate Time Series State of the Art Forecasting Graphical Model Graph Based Learning Exploiting Sparsity

June 26, 2023

Multi-output Ensembles for Multi-step Forecasting
Vitor Cerqueira, Luis Torgo
Time Series Forecasting State of the Art Forecasting Multi Step Multi Output Regression Dynamic Ensemble

June 24, 2023

Comparative Study of Predicting Stock Index Using Deep Learning Models
Harshal Patel, Bharath Kumar Bolla, Sabeesh E, Dinesh Reddy
Deep Learning Model Comparative Study Time Series Forecasting Traditional RNNs Mt RNN RNN Model Stock Index

June 21, 2023

What Constitutes Good Contrastive Learning in Time-Series Forecasting?
Chiyu Zhang, Qi Yan, Lili Meng, Tristan Sylvain
Contrastive Learning Time Series Forecasting Supervised Contrastive Learning Self Supervised Contrastive Learning

June 19, 2023

FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting
Li Shen, Yuning Wei, Yangzhu Wang, Huaxin Qiu
High Efficiency Time Series Forecasting Robust Version Skeleton Sequence F Net Focal Modulation Deep Time Series Forecasting

June 12, 2023

MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
Xing Wang, Zhendong Wang, Kexin Yang, Junlan Feng, Zhiyan Song, Chao Deng, Lin zhu
Time Series Forecasting Multivariate Time Series Forecasting Long Term Time Series Forecasting Pattern Mining

June 11, 2023

Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach
Rosana C. B. Rego
Machine Learning Time Series Forecasting Predictive Model Forecasting Algorithm Menstrual Cycle

June 4, 2023

Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
Donghao Luo, Xue Wang
Time Series Forecasting Multivariate Time Series State of the Art Forecasting Multivariate Time Series Forecasting

June 1, 2023

An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung Phan, Roman Vaculin, Jayant Kalagnanam
End to End Time Series Forecasting Forecast Utterance Inference Framework Forecasting Task

May 31, 2023

EAMDrift: An interpretable self retrain model for time series
Gonçalo Mateus, Cláudia Soares, João Leitão, António Rodrigues
Human Prediction Time Series Time Series Forecasting Time Series Prediction Self Interpretable Model

May 25, 2023

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park
Machine Learning Strong Generalization Financial Application Time Series Forecasting Model Ensembling Theoretical Guarantee Shallow Learning Weighted Ensemble

May 23, 2023

Deep Learning based Forecasting: a case study from the online fashion industry
Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekić, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski
Deep Learning Case Study Data Driven Time Series Forecasting State of the Art Forecasting Demand Forecasting Deep Learning Forecasting

May 22, 2023

Forecasting Irregularly Sampled Time Series using Graphs
Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme
Graph Neural Network Graph Drawing Time Series Time Series Forecasting State of the Art Forecasting Forecasting Performance Sparsity Structure

May 20, 2023

CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Wang Xue, Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding, Rong Jin
Transformer Model Time Series Forecasting Channel Attention Time Series Forecasting Method

May 14, 2023

HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time Series Forecasting
Ryo Umagami, Yu Ono, Yusuke Mukuta, Tatsuya Harada
Time Series Forecasting Forecasting Performance Swap Distance Minimization Permutation Invariance Permutation Equivariance

May 11, 2023

How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan
Time Series Forecasting Temporal Graph Spectral Graph

May 8, 2023

May 2, 2023

Forecast reconciliation for vaccine supply chain optimization
Bhanu Angam, Alessandro Beretta, Eli De Poorter, Matthieu Duvinage, Daniel Peralta
Time Series Forecasting Integrated Moving Average Hierarchical Time Series Supply Chain Optimization Hierarchical Time Series Forecasting Forecast Reconciliation

April 17, 2023

Long-term Forecasting with TiDE: Time-series Dense Encoder
Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu
Time Series Time Series Forecasting Multi Layer Encoder Decoder Model Long Term Time Series Forecasting Long Term Forecasting Dense Encoder