Results 241 to 250 of about 147,000 (286)

Comparison between statistical models and machine learning for forecasting multivariate time series: An empirical approach

Communications in Statistics: Case Studies, Data Analysis and Applications
Time series forecasting is a classic area of study in statistics and a growing field in Machine Learning. This paper aims to compare the performance of classic statistical models–Vector Autoregressive (VAR) and Vector Error Correction Model (VECM)–with ...
J. Mariño, Mario E. Arrieta-Prieto, S. Calderón V.   +2 more
semanticscholar   +1 more source

A Hybrid Deep Learning Approach for Multi‐Output Short‐Term Electricity Demand Forecasting

Concurrency and Computation
This study proposes hybrid deep learning architectures that integrate convolutional and recurrent layers for short‐term electricity demand forecasting. A multivariate half‐hourly dataset from Great Britain's National Grid Electricity System Operator (ESO)
Yıldırım Özüpak, Shuhratjon Mansurov   +1 more
semanticscholar   +1 more source

Interpretable forecasting of dissolved oxygen leveraging foundation model for proactive aeration in rural wastewater treatment systems.

Water Research
Accurate forecasting of dissolved oxygen (DO) is crucial for maintaining biological treatment and minimizing energy consumption in wastewater systems.
Jeimy L Martinez De La Hoz, M. Bappy, MD Shafikul Islam, Mason Marcantel, Michael P. Hayes   +4 more
semanticscholar   +1 more source

Stock Price Forecasting Using Autoregressive With Exogenous Variable Support Vector Regression (ARX – SVR)

Jurnal Matematika Statistika dan Komputasi
Stock prices move fluctuate continuously and dynamically at all times, so stock price predictions are needed to maximize profits for investors and avoid losses due to the characteristic of stock prices.
E. Widyaningrum, Rizka Amalia Putri, Morina A. Fathan, Nur Rezky Safitriani   +3 more
semanticscholar   +1 more source

Extrapolation to the complete basis-set limit in density-functional theory using statistical learning

PHYSICAL REVIEW MATERIALS
The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached in the limit of a complete basis set (CBS). Our
Daniel T. Speckhard, Christian Carbogno, L. Ghiringhelli, Sven Lubeck, Matthias Scheffler, C. Draxl   +5 more
semanticscholar   +1 more source

A Study of Machine Learning Model's Performance for Load Forecasting Based on XAI Techniques

International Conference on Computer and Automation Engineering
Accurate load forecasting is crucial for efficient management, planning, and operation of modern power systems, especially in an era of increasing electrical demand driven by technological advancements and environmental changes.
Christopher M. Dean, Xuzhe Ma, Tong Ding, Ke Xu, Yujia Chen, Huadong Mo   +5 more
semanticscholar   +1 more source

Calibration estimation of population mean in stratified sampling using standard deviation

Quality & Quantity: International Journal of Methodology, 2023
O. Babatunde, A. Oladugba, I. O. Ude, Ayodeji S. Adubi   +3 more
semanticscholar   +1 more source

A Hybrid Prediction Model Using Statistical Forecasters and Deep Neural Networks

Applied Sciences
The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others.
Renan Otvin Klehm, W. Parreira, R. Dazzi, Anita Fernandes, David Cruz García, G. V. González   +5 more
semanticscholar   +1 more source

Hybrid Deep Learning Models for Energy Consumption Forecasting: A CNN-LSTM Approach for Large-Scale Datasets

Journal of Renewable Energy and Smart Grid Technology
Long-term electricity consumption forecasting is essential with the rise of smart grids and advanced metering infrastructures for optimized energy management.
Sriharish Nandigam, K. Nageswararao, Purnima K. Sharma   +2 more
semanticscholar   +1 more source

Wind Power Forecasting Method Based on Bidirectional Long Short-Term Memory Neural Network and Error Correction

, 2022
W. Liu, Yuming Liu, Lei Fu, Minghui Yang, Ren Hu, Yan Kang   +5 more
semanticscholar   +1 more source