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

September 15, 2022

FRANS: Automatic Feature Extraction for Time Series Forecasting
Alexey Chernikov, Chang Wei Tan, Pablo Montero-Manso, Christoph Bergmeir
Time Series Forecasting Feature Extraction Predictive Capability Autoregressive Search Engine

September 1, 2022

Progressive Fusion for Multimodal Integration
Shiv Shankar, Laure Thompson, Madalina Fiterau
Time Series Forecasting Multimodal Information Fusion Feature Representation Fusion Multimodal Integration

August 30, 2022

Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting
Espen Haugsdal, Erlend Aune, Massimiliano Ruocco
Transformer Architecture Transformer Model Time Series Forecasting Normalization Dictionary New Initialization Autoregressive Neural Network

August 29, 2022

Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Time Series Forecasting Multi Step Graph Network Wind Speed Novel Transformer Architecture

August 26, 2022

Static Seeding and Clustering of LSTM Embeddings to Learn from Loosely Time-Decoupled Events
Christian Manasseh, Razvan Veliche, Jared Bennett, Hamilton Clouse
Long Short Term Memory Time Series Forecasting Event Description Seed Area

August 17, 2022

ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario
Jin Huang, Bosong Huang, Weihao Yu, Jing Xiao, Ruzhong Xie, Ke Ruan
Time Series Forecasting Temporal Dependency Long Sequence Temporal Transformer Origin Destination

July 27, 2022

Time Series Forecasting Models Copy the Past: How to Mitigate
Chrysoula Kosma, Giannis Nikolentzos, Nancy Xu, Michalis Vazirgiannis
Loss Function Time Series Forecasting Neural Network Model Regularization Term

July 16, 2022

CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
Mingjie Li, Rui Liu, Guangsi Shi, Mingfei Han, Changling Li, Lina Yao, Xiaojun Chang, Ling Chen
Transformer Based Model Time Series Forecasting Long Sequence Information Redundancy Decoder Only LLM

July 13, 2022

Learning Deep Time-index Models for Time Series Forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
Time Series Forecasting Time Index

July 12, 2022

Dateformer: Time-modeling Transformer for Longer-term Series Forecasting
Julong Young, Junhui Chen, Feihu Huang, Jian Peng
Time Series Forecasting Long Term Time Series Forecasting Temporal Representation Time Transformer

July 3, 2022

Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction
Muhammad Bilal, Hyeok Kim, Muhammad Fayaz, Pravin Pawar
Comparative Study Time Series Forecasting Energy Consumption Energy Prediction Accurate Energy Time Series Forecasting Method

June 26, 2022

fETSmcs: Feature-based ETS model component selection
Lingzhi Qi, Xixi Li, Qiang Wang, Suling Jia
Time Series Forecasting Model Selection Variable Selection Exponential Smoothing

June 25, 2022

Multi-Variate Time Series Forecasting on Variable Subsets
Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran
Scientific Inference Time Series Forecasting Multivariate Time Series Forecasting Forecasting Model Inference Task High Quality Subset

June 11, 2022

DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting
Benhan Li, Shengdong Du, Tianrui Li, Jie Hu, Zhen Jia
Transformer Based Model Time Series Forecasting Attention Based Model Attention Model

June 8, 2022

May 28, 2022

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long
Time Series Forecasting

May 26, 2022

Are Transformers Effective for Time Series Forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu
Transformer Megatron Decepticons Transformer Based Time Series Forecasting Long Term Time Series Forecasting Temporal Relation

May 25, 2022

Forecasting Patient Demand at Urgent Care Clinics using Machine Learning
Paula Maddigan, Teo Susnjak
Machine Learning Time Series Forecasting Demand Forecasting Pandemic Lockdown Sale Pattern

May 18, 2022

Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics
Maysa M. Garcia de Macedo, Wyatt Clarke, Eli Lucherini, Tyler Baldwin, Dilermando Queiroz Neto, Rogerio de Paula, Subhro Das
Representation Learning Time Series Forecasting Temporal Dynamic Multi Step Labor Market Skill Demand

Time Series Forecasting

Papers

FRANS: Automatic Feature Extraction for Time Series Forecasting

Progressive Fusion for Multimodal Integration

Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting

Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures

Static Seeding and Clustering of LSTM Embeddings to Learn from Loosely Time-Decoupled Events

ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario

Time Series Forecasting Models Copy the Past: How to Mitigate

CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System

Learning Deep Time-index Models for Time Series Forecasting

Dateformer: Time-modeling Transformer for Longer-term Series Forecasting

Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction

fETSmcs: Feature-based ETS model component selection

Multi-Variate Time Series Forecasting on Variable Subsets

DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting

Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

Unsupervised Knowledge Adaptation for Passenger Demand Forecasting

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

Are Transformers Effective for Time Series Forecasting?

Forecasting Patient Demand at Urgent Care Clinics using Machine Learning

Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics