Time Series Forecasting Method

Time series forecasting aims to predict future values of a variable based on its historical data, a crucial task across diverse fields. Current research emphasizes improving model accuracy and robustness, particularly focusing on architectures like Transformers and novel approaches incorporating spatial and temporal dependencies, as well as handling anomalies and incorporating exogenous variables. This active area of research is driven by the need for more accurate and reliable predictions in applications ranging from resource management (e.g., energy, water) to financial modeling and anomaly detection in complex systems. Benchmarking efforts are also underway to facilitate fair comparisons and guide method selection for optimal performance in various contexts.

Papers

November 18, 2024

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors
Ronghui Han, Duanyu Feng, Hongyu Du, Hao Wang
Loss Function NCD Method Time Series Forecasting World Event Connected Component Hybrid Loss Time Series Forecasting Method Seasonal Trend Decomposition Time Series Decomposition

October 27, 2024

A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments
Wentao Gao, Cheng Zhou
Robotic System Dynamic Environment Multivariate Time Series Data Robust Prediction Time Series Forecasting Method Personalized Robot Future Behavior

October 23, 2024

Predicting Company Growth by Econophysics informed Machine Learning
Ruyi Tao, Kaiwei Liu, Xu Jing, Jiang Zhang
Machine Learning Company Name Prediction Framework Macroeconomic Model Time Series Forecasting Method

August 21, 2024

KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?
Xiao Han, Xinfeng Zhang, Yiling Wu, Zhenduo Zhang, Zhe Wu
Time Series Forecasting Kolmogorov Arnold Network KAN Layer Time Series Forecasting Method

August 15, 2024

System States Forecasting of Microservices with Dynamic Spatio-Temporal Data
Yifei Xu, Jingguo Ge, Haina Tang, Shuai Ding, Tong Li, Hui Li
Microservice Architecture Time Series Forecasting Method Spatio Temporal Relationship State Forecasting

August 8, 2024

Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon
Jiang You, Arben Cela, René Natowicz, Jacob Ouanounou, Patrick Siarry
Anomaly Detection Time Series Novel Approach New Horizon Excess Delay Time Series Forecasting Method

July 23, 2024

Can time series forecasting be automated? A benchmark and analysis
Anvitha Thirthapura Sreedhara, Joaquin Vanschoren
New Benchmark General Analysis Time Series Forecasting Forecasting Method Time Series Forecasting Method

July 17, 2024

Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting
Xingyu Zhang, Siyu Zhao, Zeen Song, Huijie Guo, Jianqi Zhang, Changwen Zheng, Wenwen Qiang
Time Series Forecasting Long Term Time Series Forecasting Long Term Forecasting Dominant Low Frequency Time Series Forecasting Method Dynamic Fusion

March 29, 2024

TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, Bin Yang
Time Series Forecasting Multivariate Time Series Forecasting Univariate Time Series Forecasting Forecasting Method Fairness Benchmark Time Series Forecasting Method

February 3, 2024

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Hao Cheng, Qingsong Wen, Yang Liu, Liang Sun
Product Design Time Series Forecasting Theoretical Understanding Unlabeled Anomaly Robust Loss Time Series Forecasting Method Robust Forecasting

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

March 18, 2023

Discovering Predictable Latent Factors for Time Series Forecasting
Jingyi Hou, Zhen Dong, Jiayu Zhou, Zhijie Liu
Time Series Time Series Forecasting Latent Factor Predictability Problem Time Series Forecasting Method

September 28, 2022

Experimental study of time series forecasting methods for groundwater level prediction
Michael Franklin Mbouopda, Thomas Guyet, Nicolas Labroche, Abel Henriot
Experimental Study Global Forecasting Time Series Forecasting Method Groundwater Level

September 21, 2022

Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
Sichen Li, Andreas Adelmann
Financial Application Narrative Review Data Driven Time Series Forecasting Multivariate Time Series Particle Accelerator Time Series Forecasting Method

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

November 5, 2021

Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts
Prathamesh Deshpande, Sunita Sarawagi
Long Horizon Time Series Forecasting Method Level Aggregation Probabilistic Forecasting

Time Series Forecasting Method

Papers

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors

A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments

Predicting Company Growth by Econophysics informed Machine Learning

KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?

System States Forecasting of Microservices with Dynamic Spatio-Temporal Data

Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon

Can time series forecasting be automated? A benchmark and analysis

Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting

TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting

Discovering Predictable Latent Factors for Time Series Forecasting

Experimental study of time series forecasting methods for groundwater level prediction

Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators

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

Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts