Time-Series Prediction

We develop deep graph time-series spatio-temporal forecasting models that leverage interdependency structures and contextual information to improve prediction accuracy.

Funded Projects

• Size-segregated Particle Odor Chromatographic Kernel (SPOCK) – IARPA 2024
• A data-driven multi-scale phytotechnology framework for identification and remediation of leached-metals-contaminated soil near coal ash impoundments – DOE 2022
• Robust Real-time Resource Forecasting for Pricing Scheme Design – Adobe 2022
• Time-series Prediction for Cloud Demand Forecasting – Adobe 2020
• Prognostics and System Health Management – Xerox 2015
• An Integrated Approach to Interactive Patient-Centric Monitoring – Xerox 2015
• Baby Data Analytics: Towards Early Detection of Autism – PARC 2014

Publications

2024

[t1] Deep Graph Learning based Relational Modeling for Time-series Forecasting
H. Chen
PhD Dissertation

[j1] Graph Time-Series Modeling in Deep Learning: A Survey
H. Chen and H. Eldardiry
ACM Transactions on Knowledge Discovery from Data (TKDD) 2024
Impact Factor 5.3

[c1] Evolving Super Graph Neural Networks for Large-scale Time-Series Forecasting
H. Chen, R. A. Rossi, K. Mahadik, S. Kim, and H. Eldardiry
ACM Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2024
Acceptance Rate 15-30%

2023

[j2] Graph Deep Factors for Probabilistic Time-Series Forecasting
H. Chen, R. A. Rossi, K. Mahadik, S. Kim, and H. Eldardiry
ACM Transactions on Knowledge Discovery from Data (TKDD) 2023
Impact Factor 5

[c2] Hypergraph Neural Networks for Time-series Forecasting
H. Chen, R. A. Rossi, K. Mahadik, S. Kim, H. Eldardiry
IEEE International Conference on Big Data 2023
Acceptance Rate 17.4%

2022

[c3] Assessing the Robustness of LSTM Neural Networks for the Prediction of Actuated-Coordinated Traffic Signal Change Times
S. Eteifa, H. A. Rakha, and H. Eldardiry
Transportation Research Board (TRB) Annual Meeting 2022
Acceptance Rate 45%

2021

[j3] Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
Code, Data, Presentation
M. Afzalan, F. Jazizadeh, and H. Eldardiry
IEEE Access 2021 Impact Factor 3.9

[j4] Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks
S. Eteifa, H.A. Rakha, and H. Eldardiry
Transportation Research Record (TRB) 2021 Impact Factor 1.9

[c4] Context Integrated Relational Spatio-Temporal Resource Forecasting
H. Chen, R. A. Rossi, K. Mahadik, and H. Eldardiry
IEEE International Conference on Big Data 2021
Acceptance Rate 19.9%

[c5] Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation
H. Chen, R. A. Rossi, K. Mahadik, S. Kim, and H. Eldardiry
ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD) 2021
Acceptance Rate 15.4%

[c6] Multistage Hybrid Attentive Networks for Knowledge-Driven Stock Movement Prediction
Code, Data, Presentation
J. Gong and H. Eldardiry
ACM International Conference on Neural Information Processing (ICONIP) 2021
Acceptance Rate 20.9%

[c7] Predicting Stock Price Movement Using Financial News Sentiment
J. Gong, B. Paye, G. Kadlec, and H. Eldardiry
International Conference on Engineering Applications of Neural Networks 2021

[c8] Predicting Coordinated Actuated Traffic Signal Change Times using LSTM Neural Networks
S. Eteifa, H. Rakha, H. Eldardiry
Transportation Research Board (TRB) Annual Meeting 2021
Paper TRBAM-21-02753
Acceptance Rate 45%

2020

[t2] Building Energy Profile Clustering Based on Energy Consumption Patterns
M. Afzalan
MS Thesis

[p1] Interactive Remote Patient Monitoring and Condition Management Intervention System
H. Eldardiry, J. Rubin, R. Abreu, S. Ahern, D. Garcia, H. Du, A. Pattekar, and D. Bobrow
PATENT 2017049374 GRANTED 2020

[j5] COVID-19 Pandemic Impacts on Traffic System Delay, Fuel Consumption, and Emissions
J. Du, H.A. Rakha, F Filali, and H. Eldardiry
International Journal of Transportation Science and Technology 2020
Impact Factor 4.3
Cite Score 7.2
Most Cited Award

2018

[p2] Device Health Estimation by Combining Contextual Information with Sensor Data
H. Eldardiry, L. Liao, T. Honda, B. Saha, and R. Abreu
PATENT 10078062 GRANTED 2018

2016

[c9] Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series
J. Rubin, R. Abreu, S. Ahern, H. Eldardiry, and D. Bobrow
ACM Pervasive Health Conference (PHC) 2016

2015

[c10] Towards a Mobile and Wearable System for Predicting Panic Attacks
J. Rubin, H. Eldardiry, R. Abreu, S. Ahern, D. Garcia, H. Du, and A. Pattekar
ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp) 2015
Acceptance Rate 25.6%

[c11] A Wearable and Mobile Intervention Delivery System for Individuals with Panic Disorder
L. Cruz, J. Rubin, R. Abreu, S. Ahern, H. Eldardiry, and D. Bobrow
ACM International Conference on Mobile and Ubiquitous Multimedia (MUM) 2015
Acceptance Rate 37.1%

[c12] Device Health Estimation by Combining Contextual Control Information with Sensor Data
T. Honda, L. Liao, H. Eldardiry, B. Saha, and R. Abreu
International Workshop on Principles of Diagnosis (DX) 2015