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

January 14, 2025

Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin
Joao Carmo de Almeida Neto, Claudio Miceli de Farias, Leandro Santiago de Araujo, Leopoldo Andre Dutra Lusquino Filho
Generative Model Time Series GAN Model Time Series Forecasting Digital Twin LSTM Network Generative Adversarial Time Series Model Satellite Telemetry BiLSTM CNN CRF

January 13, 2025

Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
Xin Zhou, Weiqing Wang, Shilin Qu, Zhiqiang Zhang, Christoph Bergmeir
Time Series Text Modality Time Series Forecasting Full Potential State of the Art Forecasting High Dimensional Time Series Time Series Model

January 9, 2025

Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
HyunGi Kim, Siwon Kim, Jisoo Mok, Sungroh Yoon
Time Series Time Series Forecasting Adaptation Concern Test Time Adaptation Test Time Long Term Forecasting Non Stationarity

January 2, 2025

Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction
Syed Tahir Hussain Rizvi, Neel Kanwal, Muddasar Naeem, Alfredo Cuzzocrea, Antonio Coronato
Transformer Based Model Time Series Forecasting Architecture Design Time Series Prediction Linear Predictor

December 24, 2024

Neural Conformal Control for Time Series Forecasting
Ruipu Li, Alexander Rodríguez
Human Prediction Time Series Forecasting Adaptive Importance Conformal Prediction Neural Network Controller Prediction Interval

December 23, 2024

December 22, 2024

WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting
Md Mahmuddun Nabi Murad, Mehmet Aktukmak, Yasin Yilmaz
Time Series Time Series Forecasting State of the Art Forecasting MLP Mixer

December 21, 2024

TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
Silin Yang, Dong Wang, Haoqi Zheng, Ruochun Jin
Large Language Model Time Series Retrieval Augmented Generation Faithful Generation Time Series Forecasting LLM Based Dynamic Time Warping

December 19, 2024

Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
Luis Roque, Carlos Soares, Vitor Cerqueira, Luis Torgo
Data Set Time Series Forecasting Benchmark Datasets Cherry Picking

December 18, 2024

QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld, Jayne Thompson, Mile Gu
Time Series Forecasting Quantum Machine Learning Multivariate Time Series Forecasting Long Term Time Series Forecasting

December 17, 2024

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting
Nicholas Kiefer, Arvid Weyrauch, Muhammed Öz, Achim Streit, Markus Götz, Charlotte Debus
Comparative Study Time Series Forecasting State of the Art Forecasting Pruning Method Structured Pruning Predictive Performance Time Series Model

December 16, 2024

December 15, 2024

Learning Latent Spaces for Domain Generalization in Time Series Forecasting
Songgaojun Deng, Maarten de Rijke
Time Series Domain Generalization Time Series Forecasting State of the Art Forecasting

December 12, 2024

December 11, 2024

December 9, 2024

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz
Time Series Forecasting Temporal Modeling Multi Scale Decomposition Forecasting Architecture

Time Series Forecasting

Papers

Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin

Unveiling the Potential of Text in High-Dimensional Time Series Forecasting

Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction

Neural Conformal Control for Time Series Forecasting

EasyTime: Time Series Forecasting Made Easy

Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning

WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting

TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation

Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine

QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting

EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions

Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting

Learning Latent Spaces for Domain Generalization in Time Series Forecasting

Auto-Regressive Moving Diffusion Models for Time Series Forecasting

A Decomposition Modeling Framework for Seasonal Time-Series Forecasting

Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting

LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting