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

2016 ◽  
Vol 44 (12) ◽  
pp. 87-87 ◽  
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

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

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

scholarly journals A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data

2015 ◽  
Vol 21 (10) ◽  
pp. 3037-3041 ◽  
Author(s):  
. Haviluddin ◽  
Rayner Alfred ◽  
Joe Henry Obit ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Ag Asri Ag Ibrahim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Learning Time ◽  
Learning Techniques ◽  
A Performance

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

2021 ◽  
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 ◽  
Time Series Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Feature Importance

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.

scholarly journals Different Techniques used in Stock Market Prediction

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.

Development of A Drug Early Warning System Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Warning System ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a Drug Early Warning System Model (DEWSM), it included drug injections and vital signs as this research important features. We use it to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose this new model for detecting cardiac arrest via drug classification and by using a sliding window; we apply learning-based algorithms to time-series data for a DEWSM. By treating drug features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model, we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits (intravenous therapy), and replenishers and regulators of water and electrolytes (fluid and electrolyte supplement). The best AUROC of bits is 85%, it means the medical expert suggest the drug features: bits, it will affect the vital signs, and then the evaluate this model correctly classified patients with CPR reach 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. It can be seen that the use of new AI technology will achieve better results, currently comparable to the accuracy of traditional common RF, and the LSTM model can be adjusted in the future to obtain better results. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. The National Early Warning Score (NEWS) only focuses on the score of vital signs, and does not include factors related to drug injections. In this study, the experimental results of adding the drug injections are better than only vital signs. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, we use traditional machine learning methods and deep learning (using LSTM method as the main processing time series data) as the basis for comparison of this research. The proposed DEWSM, which offers 4-hour predictions, is better than the NEWS in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

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