Please use this identifier to cite or link to this item:
Title: An end-to-end trainable feature selection-forecasting architecture targeted at the internet of things
Authors: Nakıp, Mert
Karakayalı, Kubilay
Güzeliş, Cüneyt
Rodoplu, Volkan
Keywords: Forecasting
Feature extraction
Computer architecture
Internet of things
Smart cities
Performance evaluation
Neural networks
Publisher: IEEE
Abstract: We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection, forecasting) technique pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic, automatic feature selection capability. In addition, we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.
ISSN: 2169-3536
Appears in Collections:IZTECH Research Centers Collection / İYTE Araştırma Merkezleri Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
An_End-to-End_Trainable_Feature.pdf4.33 MBAdobe PDFView/Open
Show full item record

CORE Recommender


checked on Apr 5, 2024


checked on Mar 23, 2024

Page view(s)

checked on Apr 22, 2024


checked on Apr 22, 2024

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.