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Publications: Time-Series

PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series

Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved eithe

Irregularity Encoders Parallelised Encoders Forecasting Heterogeneous Time-Series

ESPRESSO: entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data

Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity R

ESPRESSO entropy Time-Series segmentation

Keywords

sensor [21]
mobile [12]
behaviour [10]
Occupancy Prediction [9]
Pattern recognition [9]
trajectory [9]
Analysis [9]
Sensor Data [8]
context [8]
time series [8]

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Publications: Time-Series

PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series

ESPRESSO: entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data