Forecasting Analysis

Forecasting analysis aims to predict future values of time series data, driven by the need for accurate predictions across diverse domains. Current research emphasizes improving forecasting accuracy and robustness through advanced machine learning models, including gradient boosted trees, neural networks (like LSTMs and Transformers), and graph neural networks, often incorporating multi-modal data and causal analysis to enhance interpretability and reliability. These advancements are impacting various fields, from supply chain management and air quality prediction to financial markets and emergency services, enabling better resource allocation and decision-making. The focus is on developing more efficient and robust models that can handle complex temporal dynamics and imbalanced datasets.

Papers

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

June 12, 2024

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng, Parisa Zehtabi, Sunandita Patra, Biplav Srivastava, Marco Valtorta
Native Robustness Time Series Forecasting Human Rating Forecasting Analysis Robust Forecasting

April 8, 2024

Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates
Antonio Giganti, Sara Mandelli, Paolo Bestagini, Umberto Giuriato, Alessandro D'Ausilio, Marco Marcon, Stefano Tubaro
Future Reasoning Spatiotemporal Representation Prediction Horizon Air Quality Forecasting Time Varying Covariates Spatiotemporal Graph Neural Network Forecasting Analysis

October 19, 2023

Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series
Ioannis Nasios, Konstantinos Vogklis
Neural Network Machine Learning Model Tree Specie Probabilistic Forecast Deep Learning Track Hierarchical Time Series Forecasting Analysis

October 11, 2023

Survey on Imbalanced Data, Representation Learning and SEP Forecasting
Josias Moukpe
Timely Survey Representation Learning Imbalanced Data Data Imbalance Data Balancing Forecasting Analysis

August 31, 2023

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input
Jalmari Tuominen, Eetu Pulkkinen, Jaakko Peltonen, Juho Kanniainen, Niku Oksala, Ari Palomäki, Antti Roine
Electronic Health Record Forecasting Model Demand Prediction Variable Model Advanced Machine Learning Forecasting Analysis

July 24, 2023

Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
Md Abrar Jahin, Md Sakib Hossain Shovon, Jungpil Shin, Istiyaque Ahmed Ridoy, M. F. Mridha
Machine Learning State of the Art Forecasting Big Data Inventory Management Data Preprocessing Supply Chain Resilience Forecasting Analysis Forecasting Strategy

July 13, 2023

Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks
Mario Di Mauro, Giovanni Galatro, Fabio Postiglione, Wei Song, Antonio Liotta
Multivariate Time Series State of the Art Forecasting Network Traffic Mobile Network Quality of Experience Vector Autoregression Forecasting Analysis

May 28, 2023

Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers
Zahra Atashgahi, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu
Training Data Transformer Megatron Decepticons Sparse Neural Network Sparse Training Forecasting Analysis Efficient Time Series Forecasting Adaptive Sparsity Sparsity Tradeoff

April 11, 2023

Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine
Tatiana Gonzalez Grandon, Johannes Schwenzer, Thomas Steens, Julia Breuing
Case Study Machine Learning Algorithm Efficient Hybrid Short Term Consumption Forecasting Russo Ukrainian Forecasting Analysis

June 30, 2022

Forecasting Future World Events with Neural Networks
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks
Language Model Neural Network State of the Art Forecasting Forecasting Task Event Forecasting Forecasting Analysis

Forecasting Analysis

Papers

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

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens

Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates

Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series

Survey on Imbalanced Data, Representation Learning and SEP Forecasting

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input

Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks

Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers

Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine

Forecasting Future World Events with Neural Networks