Data-driven prediction in dynamical systems: recent developments
A Ghadami, BI Epureanu - Philosophical Transactions of …, 2022 - royalsocietypublishing.org
In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …
larger-scale systems in the majority of the grand societal challenges tackled in applied …
Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy
The proliferation of sensing technologies has allowed the collection of occupancy-related
data to support various building applications, including adaptive HVAC and lighting controls …
data to support various building applications, including adaptive HVAC and lighting controls …
Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN
Short-term rainfall forecasting plays an important role in hydrologic modeling and water
resource management problems such as flood warning and real time control of urban …
resource management problems such as flood warning and real time control of urban …
Predicting energy consumption using LSTM, multi-layer GRU and drop-GRU neural networks
S Mahjoub, L Chrifi-Alaoui, B Marhic, L Delahoche - Sensors, 2022 - mdpi.com
With the steep rise in the development of smart grids and the current advancement in
developing measuring infrastructure, short term power consumption forecasting has recently …
developing measuring infrastructure, short term power consumption forecasting has recently …
Retracted: Weather forecasting and prediction using hybrid C5. 0 machine learning algorithm
S Murugan Bhagavathi, A Thavasimuthu… - International Journal …, 2021 - Wiley Online Library
In this research, a weather forecasting model based on machine learning is proposed for
improving the accuracy and efficiency of forecasting. The aim of this research is to propose a …
improving the accuracy and efficiency of forecasting. The aim of this research is to propose a …
Machine Learning for public transportation demand prediction: A Systematic Literature Review
Abstract Within the Intelligent Public Transportation Systems (IPTS) field, the prediction of
public transportation demand is a key point for enhancing the quality of the services. These …
public transportation demand is a key point for enhancing the quality of the services. These …
Forecast methods for time series data: a survey
Z Liu, Z Zhu, J Gao, C Xu - Ieee Access, 2021 - ieeexplore.ieee.org
Research on forecasting methods of time series data has become one of the hot spots. More
and more time series data are produced in various fields. It provides data for the research of …
and more time series data are produced in various fields. It provides data for the research of …
DAFA-BiLSTM: Deep autoregression feature augmented bidirectional LSTM network for time series prediction
H Wang, Y Zhang, J Liang, L Liu - Neural Networks, 2023 - Elsevier
Time series forecasting models that use the past information of exogenous or endogenous
sequences to forecast future series play an important role in the real world because most …
sequences to forecast future series play an important role in the real world because most …
Analysis of environmental factors using AI and ML methods
The main goal of this research paper is to apply a deep neural network model for time series
forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are …
forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are …
A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network
H Niu, K Xu, W Wang - Applied Intelligence, 2020 - Springer
Abstract Changes in the composite stock price index are a barometer of social and
economic development. To improve the accuracy of stock price index prediction, this paper …
economic development. To improve the accuracy of stock price index prediction, this paper …