Details

Autor(en) / Beteiligte

• Tzagkarakis, Christos
• Charalampidis, Pavlos
• Roubakis, Stylianos
• Fragkiadakis, Alexandros
• Ioannidis, Sotiris

Titel

Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments

Ist Teil von

• 2022 30th European Signal Processing Conference (EUSIPCO), 2022, p.1116-1120

Ort / Verlag

EUSIPCO

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.