Parallel spatio-temporal attention-based TCN for multivariate time series prediction
J Fan, K Zhang, Y Huang, Y Zhu, B Chen - Neural Computing and …, 2023 - Springer
As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …
surveillance to our health become more ubiquitous, multivariate time series prediction is …
Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders
Energy consumption in buildings is increasing because of social development and
urbanization. Forecasting the energy consumption in buildings is essential for improving …
urbanization. Forecasting the energy consumption in buildings is essential for improving …
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
MHDM Ribeiro, L dos Santos Coelho - Applied soft computing, 2020 - Elsevier
The investigation of the accuracy of methods employed to forecast agricultural commodities
prices is an important area of study. In this context, the development of effective models is …
prices is an important area of study. In this context, the development of effective models is …
Clustering-based anomaly detection in multivariate time series data
Multivariate time series data come as a collection of time series describing different aspects
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey
Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …
availability of a historical dataset for model development, and that the resulting model will, to …
[HTML][HTML] Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine
J Zhang, Q Yan, X Zhu, K Yu - Digital Communications and Networks, 2023 - Elsevier
The crowd sensing technology can realize the sensing and computing of people, machines,
and environment in smart industrial IoT-based coal mine, which provides a solution for …
and environment in smart industrial IoT-based coal mine, which provides a solution for …
A review on machine learning forecasting growth trends and their real-time applications in different energy systems
T Ahmad, H Chen - Sustainable Cities and Society, 2020 - Elsevier
Energy forecasting and planning play an important role in energy sector development and
policy formulation. The forecasting model selection mostly based on the availability of the …
policy formulation. The forecasting model selection mostly based on the availability of the …
A tinyml soft-sensor approach for low-cost detection and monitoring of vehicular emissions
Vehicles are the major source of air pollution in modern cities, emitting excessive levels of
CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles …
CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles …
A hybrid approach to motion prediction for ship docking—Integration of a neural network model into the ship dynamic model
While automatic controllers are frequently used during transit operations and low-speed
maneuvering of ships, ship operators typically perform docking maneuvers. This task is more …
maneuvering of ships, ship operators typically perform docking maneuvers. This task is more …
Autonomous learning for fuzzy systems: a review
As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …