Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments
Creators
- 1. Foundation for Research and Technology-Hellas, Institute of Computer Science, Heraklion, Greece
- 2. oundation for Research and Technology-Hellas, Institute of Computer Science, Heraklion, Greece
- 3. echnical University of Crete, Chania, Greece & Foundation for Research and Technology-Hellas, Institute of Computer Science, Heraklion, Greece
Description
Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and energy re- sources. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies, yielding a reduced set of observations. Nevertheless, this can hamper dramatically subsequent decision-making, such as forecasting. To address this problem, in this work we evaluate short-term forecasting in highly underdetermined cases, i.e., the number of sensor streams is much higher than the number of observations. Several statistical, machine learning and neural network-based models are thoroughly examined with respect to the resulting forecasting accuracy on five different real-world datasets. The focus is given on a unified experimental protocol especially designed for short-term prediction of multiple time series at the IoT edge. The proposed framework can be considered as an important step towards establishing a solid forecasting strategy in resource constrained IoT applications.
Notes
Files
EUSIPCO2022_Tzagkarakis_et_al.pdf
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Related works
- Is supplemented by
- Software: https://github.com/pcharala/multiple-timeseries-forecasting (URL)