Forecasting Performance

Forecasting performance research aims to improve the accuracy and efficiency of predicting future events across diverse domains, from weather and finance to energy grids and public health. Current research emphasizes developing and refining model architectures like transformers, LSTMs, and hybrid models incorporating techniques such as Low-Rank Adaptation and Variational Autoencoders, often coupled with feature engineering and data augmentation strategies to enhance predictive power. These advancements have significant implications for various sectors, enabling better resource allocation, risk management, and decision-making based on more reliable predictions. Furthermore, research is exploring the integration of large language models and the potential for combining human expertise with AI-driven forecasts.

Papers

November 21, 2024

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
Shourya Bose, Yijiang Li, Amy Van Sant, Yu Zhang, Kibaek Kim
Foundation Model Empirical Study Data Heterogeneity Traditional RNNs Forecasting Performance Building Energy Synthetic Power

November 15, 2024

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere
Fenghua Ling, Kang Chen, Jiye Wu, Tao Han, Jing-Jia Luo, Wanli Ouyang, Lei Bai
Deep Learning Model Time Series Forecasting High Resolution Forecasting Performance Forecast Utterance Global Weather

November 12, 2024

EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods
Xiangyu Shi, Hongcheng Ding, Salaar Faroog, Deshinta Arrova Dewi, Shamsul Nahar Abdullah, Bahiah A Malek
Language Model Deep Learning Forecasting Performance Text Mining Real Time Prediction

October 30, 2024

Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks
Matteo Citterio, Marco D'Errico, Gabriele Visentin
Network Programming Forecasting Performance Dynamic Graph Neural Network Network Dynamic Margin Loss Financial Network Transaction Network

October 25, 2024

Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization
Eduardo Luiz Alba, Matheus Henrique Dal Molin Ribeiro, Gilson Adamczuk, Flavio Trojan, Erick Oliveira Rodrigues
Machine Learning Model Metaheuristic Algorithm Forecasting Performance Random Forest Model Consumption Forecasting Support Vector Regression Weather Variable Educational Content University Campus

October 21, 2024

Forecasting Company Fundamentals
Felix Divo, Eric Endress, Kevin Endler, Kristian Kersting, Devendra Singh Dhami
Deep Learning Model Time Series Forecasting Forecasting Performance Financial Forecasting

October 19, 2024

Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting
Tugrul Cabir Hakyemez, Omer Adar
Bayesian Optimization Hyperparameter Optimization Level Test Forecasting Performance Short Term Load Forecasting Surrogate Model Training

October 17, 2024

FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling
Jintao Zhang, Mingyue Cheng, Xiaoyu Tao, Zhiding Liu, Daoyu Wang
Diffusion Model Time Series Forecasting Forecasting Performance Trend Filtering

October 14, 2024

Echo State Networks for Spatio-Temporal Area-Level Data
Zhenhua Wang, Scott H. Holan, Christopher K. Wikle
Forecasting Performance Temporal Data Temporal Dynamic Echo State Network Spatiotemporal Datasets State Occupancy Matching

October 13, 2024

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
Yanru Sun, Zongxia Xie, Emadeldeen Eldele, Dongyue Chen, Qinghua Hu, Min Wu
LeArning Abstract Time Series Time Series Forecasting Forecasting Performance Long Term Forecasting Real World Time Series

September 18, 2024

Precise Forecasting of Sky Images Using Spatial Warping
Leron Julian, Aswin C. Sankaranarayanan
Forecasting Performance Astronomical Image Solar Irradiance Feature Warping Sky Image Datasets

September 16, 2024

AALF: Almost Always Linear Forecasting
Matthias Jakobs, Thomas Liebig
Deep Learning Model Time Series Forecasting Forecasting Performance Interpretable Forecast Online Model Selection Linear Forecasting

September 11, 2024

Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region
Muhammad Akhtar Munir, Fahad Shahbaz Khan, Salman Khan
Case Study Numerical Weather Prediction Forecasting Performance Weather Forecasting Climate Model Local Adaptivity Area Modeling Neural Weather Model

September 10, 2024

VE: Modeling Multivariate Time Series Correlation with Variate Embedding
Shangjiong Wang, Zhihong Man, Zhenwei Cao, Jinchuan Zheng, Zhikang Ge
Multivariate Time Series Multivariate Time Series Forecasting Forecasting Performance Temporal Pattern Multivariate Forecasting Bivariate Observational Data

August 29, 2024

Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer
Xiaorui Xue, Shaofang Li, Xiaonan Wang
State of the Art Forecasting Forecasting Performance Stock Price Forecasting Variational Mode Decomposition Stock Price Scale Aware Dynamical Mode Recognition

August 26, 2024

Efficient fine-tuning of 37-level GraphCast with the Canadian global deterministic analysis
Christopher Subich
Data Driven Forecasting Performance Global Model Efficient Fine Tuning Spatiotemporal Forecasting

August 19, 2024

Partial-Multivariate Model for Forecasting
Jaehoon Lee, Hankook Lee, Sungik Choi, Sungjun Cho, Moontae Lee
Forecasting Performance Time Series Feature Multivariate Model

August 4, 2024

SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
Ahmed Z Naser, MZ Naser
Human Prediction Time Series Time Series Forecasting Predictive Model Time Series Data Forecasting Performance Forecasting Analysis Similarity Prediction

August 2, 2024

Enhancing Financial Market Predictions: Causality-Driven Feature Selection
Wenhao Liang, Zhengyang Li, Weitong Chen
Feature Selection Forecasting Performance Stock Market Data

June 18, 2024

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, James Zhang, Jun Zhou, Hongyuan Mei, Weitao Lin, Zi Zhuang, Wenxin Ning, Yunhua Hu, Siqiao Xue
Temporal Information Forecasting Performance Temporal Structure Temporal Granularity Hierarchical Time Series Forecasting Probabilistic Reconciliation

Forecasting Performance

Papers

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods

Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks

Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization

Forecasting Company Fundamentals

Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting

FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling

Echo State Networks for Spatio-Temporal Area-Level Data

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift

Precise Forecasting of Sky Images Using Spatial Warping

AALF: Almost Always Linear Forecasting

Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region

VE: Modeling Multivariate Time Series Correlation with Variate Embedding

Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer

Efficient fine-tuning of 37-level GraphCast with the Canadian global deterministic analysis

Partial-Multivariate Model for Forecasting

SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems

Enhancing Financial Market Predictions: Causality-Driven Feature Selection

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting