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

February 17, 2023

Forecasting with Deep Learning
Gissel Velarde
Deep Learning Time Series Time Series Forecasting State of the Art Forecasting Deep Learning Network

February 11, 2023

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu
Time Series Forecasting Multivariate Time Series Forecasting Part Whole Hierarchy Forecasting Performance Hierarchical Information Hierarchical Forecasting Hierarchical Time Series Learning Structure Single Population Optimization Task

February 9, 2023

MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu
Transformer Based Model Time Series Forecasting Temporal Information Attention Module Multivariate Time Series Forecasting Forecasting Task Simple Alternating Mixer Channel Mixing

February 6, 2023

Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust
Leonid Erlygin, Vladimir Zholobov, Valeriia Baklanova, Evgeny Sokolovskiy, Alexey Zaytsev
Gaussian Process Time Series Forecasting Uncertainty Estimation Surrogate Model Robust Model Accurate Uncertainty

February 4, 2023

Cross-Frequency Time Series Meta-Forecasting
Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy
Time Series Forecasting Frequency Representation

January 31, 2023

Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window
Yuanpeng He
Time Series Time Series Forecasting Forecasting Model Time Series Datasets Sliding Window Fuzzy Time Series

January 27, 2023

Targeted Attacks on Timeseries Forecasting
Yuvaraj Govindarajulu, Avinash Amballa, Pavan Kulkarni, Manojkumar Parmar
Adversarial Attack Time Series Time Series Forecasting Adversarial Input Targeted Attack Projected Gradient Descent Attack

January 24, 2023

Multi-view Kernel PCA for Time series Forecasting
Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
Time Series Forecasting Principal Component Analysis Kernel Ridge Regression Kernel Matrix Kernel Principal Component Analysis Kernel Machine

January 21, 2023

Ti-MAE: Self-Supervised Masked Time Series Autoencoders
Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, Zenglin Xu
Time Series Forecasting Multivariate Time Series Forecasting Masked AutoEncoder Transformer Based Generative Sce Mae Input Time Series

January 8, 2023

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang, Dejing Dou
Time Series Generative Modeling Time Series Forecasting Diffusion Explainer Disentanglement Capability Lung VAE Generative Time Series Forecasting

January 5, 2023

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution
Yan Li, Xinjiang Lu, Haoyi Xiong, Jian Tang, Jiantao Su, Bo Jin, Dejing Dou
Self Attention Transformer Model Time Series Forecasting Complex Pattern Long Term Time Series Forecasting Content Based Feature Product Distribution

December 30, 2022

Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
Tommaso Barbariol, Davide Masiero, Enrico Feltresi, Gian Antonio Susto
Anomaly Detection Time Series Forecasting Unlabeled Anomaly High Quality Sensor Multiphase Flow Anomalous Behavior

December 21, 2022

Temporal Disaggregation of the Cumulative Grass Growth
Thomas Guyet, Laurent Spillemaecker, Simon Malinowski, Anne-Isabelle Graux
Time Series Forecasting Grass Growth Cumulative Water Heater

December 15, 2022

First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang
Human Attention Time Series Forecasting Recent Trend Long Term Time Series Forecasting Attention Based Model Attention Model Seasonal Trend Decomposition Fourier Attention

December 13, 2022

Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting
Yiqi Sun, Zhengxin Shi, Jianshen Zhang, Yongzhi Qi, Hao Hu, Zuojun Max Shen
Inherent Interpretability Data Driven Time Series Forecasting Machine Learning Approach Enhancing Accuracy Decomposition Method Forecasting Algorithm Sale Forecast

December 5, 2022

cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations
Weiyu Zong, Mingqian Feng, Griffin Heyrich, Peter Chin
Time Series Forecasting Partial Observation W Net Dimensional Feature Information Approach

December 2, 2022

FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
Maowei Jiang, Pengyu Zeng, Kai Wang, Huan Liu, Wenbo Chen, Haoran Liu
Time Series Forecasting Channel Attention Fourier Transform

November 30, 2022

Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data
Sunghyun Sim, Dohee Kim, Hyerim Bae
Recurrent Neural Network Time Series Forecasting Time Series Data Neural Network Architecture Predictive Performance Recurrent Unit Time Series Decomposition

November 28, 2022