Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models

AbstractTo understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

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An Exploration and Forecast of COVID-19 in Mexico with Machine Learning

10.21203/rs.3.rs-62035/v1 ◽

2020 ◽

Author(s):

Daniela A. Gomez-Cravioto ◽

Ramon E. Diaz-Ramos ◽

Francisco J. Cantu-Ortiz ◽

Hector G. Ceballos

Keyword(s):

Machine Learning ◽

Time Series ◽

Health Care Providers ◽

Machine Learning Techniques ◽

Future Research ◽

Logistic Growth ◽

Care Providers ◽

Modern Approach ◽

Logistic Regression Models ◽

Feature Importance

Abstract Background: To understand and approach the COVID-19 spread, Machine Learning offers fundamental tools. This study presents the use of machine learning techniques for the projection of COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. Methods: The methods used are linear, polynomial, and generalized logistic regression models to evaluate the growth of the COVID-19 incidents in the country. Additionally, machine learning and time-series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with mobility rates obtained from Google’s Mobility Reports and climate variables acquired from Weather Online. Results: The results suggest that the logistic growth model fits best the behavior of the pandemic in Mexico, that there is a significant correlation of climate and mobility variables with the disease numbers, and that LSTM is a more suitable approach for the prediction of daily cases. Conclusion: We hope that this study can make some contributions to the world’s response to this epidemic as well as give some references for future research.

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2: EARLY PREDICTION OF PATIENT DETERIORATION USING MACHINE LEARNING TECHNIQUES WITH TIME SERIES DATA

Critical Care Medicine ◽

10.1097/01.ccm.0000508720.57494.7c ◽

2016 ◽

Vol 44 (12) ◽

pp. 87-87 ◽

Cited By ~ 2

Author(s):

Sareen Shah ◽

David Ledbetter ◽

Melissa Aczon ◽

Alysia Flynn ◽

Sarah Rubin

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Data ◽

Machine Learning Techniques ◽

Series Data ◽

Early Prediction ◽

Patient Deterioration ◽

Learning Techniques

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Different Techniques used in Stock Market Prediction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e9275.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 60-62

Keyword(s):

Machine Learning ◽

Time Series ◽

Stock Market ◽

Time Series Data ◽

Short Term Memory ◽

Moving Average ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Series Data ◽

Autoregressive Integrated Moving Average

The stock market has been one of the primary revenue streams for many for years. The stock market is often incalculable and uncertain; therefore predicting the ups and downs of the stock market is an uphill task even for the financial experts, which they been trying to tackle without any little success. But it is now possible to predict stock markets due to rapid improvement in technology which led to better processing speed and more accurate algorithms. It is necessary to forswear the misconception that prediction of stock market is only meant for people who have expertise in finance; hence an application can be developed to guide the user about the tempo of the stock market and risk associated with it.The prediction of prices in stock market is a complicated task, and there are various techniques that are used to solve the problem, this paper investigates some of these techniques and compares the accuracy of each of the methods. Forecasting the time series data is important topic in many economics, statistics, finance and business. Of the many techniques in forecasting time series data such as the Autoregressive, Moving Average, and the Autoregressive Integrated Moving Average, it is the Autoregressive Integrated Moving Average that has higher accuracy and higher precision than other methods. And with recent advancement in computational power of processors and advancement in knowledge of machine learning techniques and deep learning, new algorithms could be made to tackle the problem of predicting the stock market. This paper investigates one of such machine learning algorithms to forecast time series data such as Long Short Term Memory. It is compared with traditional algorithms such as the ARIMA method, to determine how superior the LSTM is compared to the traditional methods for predicting the stock market.

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Investigation of Machine Learning Techniques in Forecasting of Blood Pressure Time Series Data

Lecture Notes in Computer Science - Artificial Intelligence XXXVI ◽

10.1007/978-3-030-34885-4_21 ◽

2019 ◽

pp. 269-282 ◽

Cited By ~ 1

Author(s):

Shamsul Masum ◽

John P. Chiverton ◽

Ying Liu ◽

Branislav Vuksanovic

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Time Series ◽

Time Series Data ◽

Machine Learning Techniques ◽

Series Data ◽

Learning Techniques ◽

Pressure Time

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Time series data analysis of stock price movement using machine learning techniques

Soft Computing ◽

10.1007/s00500-020-04957-x ◽

2020 ◽

Vol 24 (21) ◽

pp. 16509-16517

Author(s):

Irfan Ramzan Parray ◽

Surinder Singh Khurana ◽

Munish Kumar ◽

Ali A. Altalbe

Keyword(s):

Machine Learning ◽

Time Series ◽

Data Analysis ◽

Stock Price ◽

Time Series Data ◽

Machine Learning Techniques ◽

Series Data ◽

Price Movement ◽

Learning Techniques ◽

Time Series Data Analysis

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Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques

Information ◽

10.3390/info11070344 ◽

2020 ◽

Vol 11 (7) ◽

pp. 344

Author(s):

Ameema Zainab ◽

Shady S. Refaat ◽

Othmane Bouhali

Keyword(s):

Machine Learning ◽

Time Series ◽

Root Mean Square Error ◽

Smart Home ◽

Time Series Data ◽

Series Data ◽

Spam Detection ◽

Mean Square ◽

Feature Importance ◽

Iot Devices

The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.

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Computational algorithms for predictive health assessments

10.32469/10355/46860 ◽

2015 ◽

Author(s):

◽

Zahra Hajihashemi

Keyword(s):

Older Adults ◽

Health Care ◽

Time Series ◽

Health Care Providers ◽

Elderly Population ◽

Time Series Data ◽

The Elderly ◽

Developed Countries ◽

Series Data ◽

Care Providers

Rapid aging of the population in the US requires increased attention from health care providers and from the entire society as a whole. While the elderly population (aged over 65) will increase by 8% until 2050 in the developed countries, the working-age population (age between 15 and 64 years) will decrease and its ratio to the elderly population will decline from 4.3 to 2.3. A possible solution to prevent unreported health problems in independently living older adults is through automatic health monitoring systems. The aim of this dissertation is to use sensor network technology to detect changes in health status of elderly living alone, alert health care providers, and augment traditional health care. In this dissertation, we address three topics. First, we discuss the problem of measuring the temporal similarity of two multidimensional time series. The second topic of this work is predicting health patterns using time series similarities. Third, we also propose three methods for identification of deviations in patterns of activities of daily livings (ADL) of older adults and use them to generate alerts for the healthcare providers. ADLs such as bathroom visits can be monitored by automated in-home sensor systems. Our proposed methods find periodicity in sensor time series data using clustering, item set mining, and statistical approaches.

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