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 22, 2024

Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Chu Myaet Thwal, Ye Lin Tun, Kitae Kim, Seong-Bae Park, Choong Seon Hong
Transformer Megatron Decepticons Time Series Time Series Forecasting Financial Time Series Federated Aggregation

January 11, 2024

Time Series Forecasting of HIV/AIDS in the Philippines Using Deep Learning: Does COVID-19 Epidemic Matter?
Sales G. Aribe, Bobby D. Gerardo, Ruji P. Medina
Deep Learning Covid 19 Time Series Forecasting

January 9, 2024

Hypercomplex neural network in time series forecasting of stock data
Radosław Kycia, Agnieszka Niemczynowicz
Neural Network Long Short Term Memory Time Series Forecasting Time Series Prediction Indian Stock Market Hypercomplex Algebra Input Time Series

January 5, 2024

The Rise of Diffusion Models in Time-Series Forecasting
Caspar Meijer, Lydia Y. Chen
Diffusion Model Generative AI Time Series Time Series Forecasting Time Series Analysis

January 4, 2024

U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
Xiang Ma, Xuemei Li, Lexin Fang, Tianlong Zhao, Caiming Zhang
Time Series Forecasting Non Stationary MLP Mixer Non Stationarity

January 3, 2024

Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels
Jiang You, Arben Cela, René Natowicz, Jacob Ouanounou, Patrick Siarry
Time Series Forecasting U Net Multivariate Time Series Forecasting Input Time Series

January 2, 2024

Explainable Adaptive Tree-based Model Selection for Time Series Forecasting
Matthias Jakobs, Amal Saadallah
Time Series Forecasting Explainability Method Explainable Model Regression Tree Tree Based Model

December 29, 2023

Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting
Raquel Espinosa, Fernando Jiménez, José Palma
Long Short Term Memory Time Series Forecasting Feature Selection LSTM Network Univariate Time Series Forecasting

December 28, 2023

TSPP: A Unified Benchmarking Tool for Time-series Forecasting
Jan Bączek, Dmytro Zhylko, Gilberto Titericz, Sajad Darabi, Jean-Francois Puget, Izzy Putterman, Dawid Majchrowski, Anmol Gupta, Kyle Kranen, Pawel Morkisz
Machine Learning Deep Learning Model Time Series Forecasting Evaluation Benchmark

December 27, 2023

FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting
Weijun Chen, Heyuan Wang, Ye Tian, Shijie Guan, Ning Liu
Time Series Forecasting Multivariate Time Series Temporal Dynamic Dependency Modeling F Net Inter Series

December 22, 2023

Deep Non-Parametric Time Series Forecaster
Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
Time Series Forecasting Deep Time Series Forecasting

December 20, 2023

AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting
Raphael Fischer, Amal Saadallah
Machine Learning Model Time Series Forecasting Multi Objective Model Selection Online autoML

December 13, 2023

auto-sktime: Automated Time Series Forecasting
Marc-André Zöller, Marius Lindauer, Marco F. Huber
Time Series Forecasting Real World Time Series Efficient Time Series Forecasting Forecasting Pipeline

December 11, 2023

TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecasting
Ling Chen, Jiahua Cui
Multi Scale Time Series Forecasting Temporal Pattern Pyramid Network

December 10, 2023

CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
Jiaxin Gao, Yuxiao Hu, Qinglong Cao, Siqi Dai, Yuntian Chen
Contrastive Learning Time Series Forecasting Contrastive Loss Time Series Representation

December 3, 2023

Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
Moisés Santos, André de Carvalho, Carlos Soares
Long Short Term Memory Faithful Generation Time Series Forecasting Synthetic Time Series Forecasting Method Algorithm Performance

November 29, 2023

Skilful Precipitation Nowcasting Using NowcastNet
Ajitabh Kumar
Time Series Forecasting Data Driven Nowcasting Precipitation Nowcasting Physic Informed Generative Physic Based Numerical Weather Prediction

November 10, 2023

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Defu Lian, Ning An, Longbing Cao, Zhendong Niu
Time Series Forecasting Frequency Learning Short Term Forecasting

November 7, 2023

Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates
Time Series Forecasting Temporal Pattern Long Term Forecasting Time Series Transformer Resolution Transformer

November 5, 2023

An adaptive standardisation methodology for Day-Ahead electricity price forecasting
Carlos Sebastián, Carlos E. González-Guillén, Jesús Juan
Time Series Forecasting Forecasting Model Electricity Market Data Standardization Dataset Shift Day Ahead