Cover Image

Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Motamedi, Maryam, Dawson, Jessica, Li, Na, Down, Douglas G., Heddle, Nancy M.
Format:	Preprint
Published:	2021
Subjects:	Machine Learning Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications
by: Xin, Ning, et al.
Published: (2025)

Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
by: Wang, Hao, et al.
Published: (2026)

Forecasting Monthly Residential Natural Gas Demand Using Just-In-Time-Learning Modeling
by: Alakent, Burak, et al.
Published: (2025)

Multivariate Time Series Data Imputation via Distributionally Robust Regularization
by: Liao, Che-Yi, et al.
Published: (2026)

Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers
by: Ji, Anli, et al.
Published: (2024)

Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2025)

Copula Conformal Prediction for Multi-step Time Series Forecasting
by: Sun, Sophia, et al.
Published: (2022)

Using Multivariate Linear Regression for Biochemical Oxygen Demand Prediction in Waste Water
by: Mutai, Isaiah K., et al.
Published: (2022)

Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram
by: Ma, Ziling, et al.
Published: (2025)

DemandLens: Enhancing Forecast Accuracy Through Product-Specific Hyperparameter Optimization
by: Pillai, Srijesh, et al.
Published: (2025)

Auto-Regressive Moving Diffusion Models for Time Series Forecasting
by: Gao, Jiaxin, et al.
Published: (2024)

Assessing the Operational Viability of Foundation Models for Time Series Forecasting
by: Soni, Kavin, et al.
Published: (2026)

AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
by: Das, Keshav, et al.
Published: (2025)

Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching
by: Xu, Xiuqin, et al.
Published: (2021)

Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
by: Ghaderi, Hamid, et al.
Published: (2024)

Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland
by: Onibonoje, Oluwadurotimi, et al.
Published: (2025)

Horizon-Aware Forecasting of Passenger Assistance Demand for Rail Station Workforce Planning
by: Sheehan, Michael, et al.
Published: (2026)

Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time Series
by: Pan, Qilong
Published: (2022)

A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts
by: Lakatos, Mária
Published: (2025)

Generative Probabilistic Time Series Forecasting and Applications in Grid Operations
by: Wang, Xinyi, et al.
Published: (2024)

CVTN: Cross Variable and Temporal Integration for Time Series Forecasting
by: Zhou, Han, et al.
Published: (2024)

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
by: Benechehab, Abdelhakim, et al.
Published: (2025)

Modeling Information Blackouts in Missing Not-At-Random Time Series Data
by: Sunesh, Aman, et al.
Published: (2026)

DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
by: Jiang, Manrui, et al.
Published: (2026)

Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting
by: Madarasingha, Chamara, et al.
Published: (2025)

Univariate Channel Fusion for Multivariate Time Series Classification
by: Moro, Fernando, et al.
Published: (2026)

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
by: Montreuil, Yannis, et al.
Published: (2026)

Efficient Causal Discovery for Autoregressive Time Series
by: Fesanghary, Mohammad, et al.
Published: (2025)

Data-Driven Bed Capacity Planning Using $M_t/G_t/\infty$ Queueing Models with an Application to Neonatal Intensive Care Units
by: Akbari-Moghaddam, Maryam, et al.
Published: (2025)

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
by: Li, Jinpeng, et al.
Published: (2026)

Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain
by: Gioia, V., et al.
Published: (2022)

Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
by: Wang, Qingyi, et al.
Published: (2023)

A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions
by: Hewage, Harsha Chamara, et al.
Published: (2025)

Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches
by: Buriticá, Gloria, et al.
Published: (2024)

Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data
by: Xie, Rui, et al.
Published: (2023)

Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
by: Hu, Annie, et al.
Published: (2024)

Transportation Marketplace Rate Forecast Using Signature Transform
by: Gu, Haotian, et al.
Published: (2024)

ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model
by: Thapa, Krishu K, et al.
Published: (2025)

Applications of Machine Learning in Pharmacogenomics: Clustering Plasma Concentration-Time Curves
by: Lautier, Jackson P., et al.
Published: (2022)

Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series
by: Hu, Yuxiao, et al.
Published: (2025)