Temporal Analysis of 911 Emergency Calls Through Time Series Modeling

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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AR and ARMA model order selection for time-series modeling with ImageNet classification

Signal Processing ◽

10.1016/j.sigpro.2021.108026 ◽

2021 ◽

Vol 183 ◽

pp. 108026

Author(s):

Jihye Moon ◽

Md Billal Hossain ◽

Ki H. Chon

Keyword(s):

Time Series ◽

Arma Model ◽

Order Selection ◽

Time Series Modeling ◽

Model Order Selection ◽

Model Order ◽

Selection For

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Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Sustainability ◽

10.3390/su13158295 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8295

Author(s):

Patricia Melin ◽

Oscar Castillo

Keyword(s):

Neural Networks ◽

Time Series ◽

Temporal Trends ◽

Main Idea ◽

Temporal Analysis ◽

Virus Propagation ◽

Self Organizing Maps ◽

Fractal Approach ◽

The Times ◽

Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

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