Cruise |

Evolutionary multivariate time series prediction

Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care domains. Over the years, various predi

Neural Evolutionary and Fuzzy Computation Multivariate time series prediction Evolutionary algorithm Ensemble Learning Feature extraction Feature selection Single-objective optimization Multi-objective optimization Pattern recognition Data Mining Neural Fuzzy time series Multivariate Optimization

Unsupervised online change point detection in high-dimensional time series

A critical problem in time series analysis is change point detection, which identifies the times when the underlying distribution of a time series abruptly changes. However, several shortcomings l

point detection high-dimensional time series time series Unsupervised change point

A Scalable Room Occupancy Prediction with Transferable Time SeriesDecomposition of CO\(_\mbox2\) Sensor Data

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - H

Scalable Room Occupancy Prediction Time Series Decomposition Occupancy Scalable time series Decomposition Occupancy Prediction

SD-HOC: Seasonal Decomposition Algorithm for Mining Lagged Time Series

Mining time series data is a difficult process due to the lag factor and different time of data arrival. In this paper, we present Seasonal Decomposition for Human Occupancy Counting (SD-HOC), a c

Decomposition Mining Lagged TimeSeries time series

A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - H

Scalable Room Occupancy Prediction Occupancy Prediction Transferable Time Series Decomposition Sensor Data time series

Unsupervised online change point detection in high-dimensional timeseries

A critical problem in time series analysis is change point detection, which identifies the times when the underlying distribution of a time series abruptly changes. However, several shortcomings l

point detection high-dimensional timeseries time series

Evolutionary Ensemble Learning for Multivariate Time Series Prediction

Multivariate time series (MTS) prediction plays a key role in many fields such as finance, energy and transport, where each individual time series corresponds to the data collected from a certain

Ensemble Learning time series Prediction

A scalable room occupancy prediction with transferable time series decomposition of CO2 sensor data

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - H

Occupancy Prediction time series Sensor Data decomposition of CO2

SEARCH ...

Publications: time series

Evolutionary multivariate time series prediction

Unsupervised online change point detection in high-dimensional time series

A Scalable Room Occupancy Prediction with Transferable Time SeriesDecomposition of CO\(_\mbox2\) Sensor Data

SD-HOC: Seasonal Decomposition Algorithm for Mining Lagged Time Series

A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data

Unsupervised online change point detection in high-dimensional timeseries

Evolutionary Ensemble Learning for Multivariate Time Series Prediction

A scalable room occupancy prediction with transferable time series decomposition of CO2 sensor data