Univariate Time Series Forecasting

Univariate time series forecasting aims to predict future values of a single time-dependent variable, crucial for diverse applications ranging from finance to energy management. Current research emphasizes improving model accuracy through advanced architectures like deep learning models (e.g., LSTMs, Transformers) and refined techniques such as optimal starting point selection, data augmentation, and ensemble methods, while also addressing explainability and robustness challenges. These advancements enhance the reliability and interpretability of forecasts, leading to more informed decision-making across various sectors and contributing to a deeper understanding of temporal dynamics in complex systems.

Papers

September 8, 2022

W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
Lena Sasal, Tanujit Chakraborty, Abdenour Hadid
Transformer Based Transformer Model Temporal Dependency Time Series Representation Learning Univariate Time Series Forecasting Transformer Based Neural Network Transformer XL

July 15, 2022

Greykite: Deploying Flexible Forecasting at Scale at LinkedIn
Reza Hosseini, Albert Chen, Kaixu Yang, Sayan Patra, Yi Su, Saad Eddin Al Orjany, Sishi Tang, Parvez Ahammad
Visual Analogue Scale State of the Art Forecasting Univariate Time Series Forecasting Interpretable Forecast LinkedIn Dataset Visual Forecasting

February 17, 2022

Multi-Objective Model Selection for Time Series Forecasting
Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
Time Series Forecasting Multi Objective Forecasting Model Univariate Time Series Forecasting Forecasting Method

January 14, 2022

IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction
Mengyue Zha, Kani Chen, Tong Zhang
Time Series Prediction Univariate Time Series Forecasting Interpretable Ensemble Competitive Learning Explainable Ensemble Interpretable Basis

December 21, 2021

AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen
Extended Version Univariate Time Series Forecasting Correlated Time Series Correlated Time Series Forecasting

December 12, 2021

Recalibrating probabilistic forecasts of epidemics
Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld
Black Box Probabilistic Forecast Epidemic Forecasting Disease Outbreak Univariate Time Series Forecasting Forecast Distribution Influenza Forecasting

November 13, 2021

LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts
Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman
New Framework Black Box Global Model Local Explanation Univariate Time Series Forecasting Global Forecasting

November 7, 2021

DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting
Tianxiang Zhan, Yuanpeng He, Hanwen Li, Fuyuan Xiao
Application Proficiency Time Series Forecasting Univariate Time Series Forecasting Visibility Graph Forecasting Strategy Index Forecasting