Multivariate Time Series Analysis and Applications

Wei, William W. S.

doi:10.1002/9781119502951

Cited by 96 publications

(137 citation statements)

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“…This seasonal pattern suggests the necessity of using other methods to forecast the number of attentions of pneumonia cases in this city. Some possible methods in the time series approach include multivariate vector autoregression models [28] or Bayesian structural time series models [29], and machine-learning methods such as recurrent neural networks [30].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Gutiérrez

2020

Preprint

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Background: Previous authors have evidenced the relationship between air pollution-aerosols and meteorological variables with the occurrence of pneumonia. Forecasting the number of attentions of pneumonia cases may be useful to optimize the allocation of healthcare resources and support public health authorities to implement emergency plans to face an increase in patients. The purpose of this study is to implement four machine-learning methods to forecast the number of attentions of pneumonia cases in the five largest cities of Colombia by using air pollution-aerosols, and meteorological and admission data.Methods: The number of attentions of pneumonia cases in the five most populated Colombian cities was provided by public health authorities between January 2009 and December 2019. Air pollution-aerosols and meteorological data were obtained from remote sensors. Four machine-learning methods were implemented for each city. We selected the machine-learning methods with the best performance in each city and implemented two techniques to identify the most relevant variables in the forecasting developed by the best-performing machine-learning models. Results: According to R2 metric, random forest was the machine-learning method with the best performance for Bogotá, Medellín and Cali; whereas for Barranquilla, the best performance was obtained from the Bayesian adaptive regression trees, and for Cartagena, extreme gradient boosting had the best performance. The most important variables for the forecasting were related to the admission data.Conclusions: The results obtained from this study suggest that machine learning can be used to efficiently forecast the number of attentions of pneumonia cases, and therefore, it can be a useful decision-making tool for public health authorities.

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Section: Discussionmentioning

confidence: 99%

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Gutiérrez

2020

Preprint

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“…Thera are eleven (11) stages to forecast with a model of transfer function [4], they are (1) Prepare input and output series, (2) Bleaching input series and output series, (3) Calculate the cross correlation between the input series and the bleached output series, (4) Direct assessment of impulse response weights, (5) Determination (r, s, b) for the transfer function model, (6) Initial estimation of noise series ) (…”

Section: Identify the Transfer Function Modelmentioning

confidence: 99%

“…There is a delay value of 6 and the patterned cross correlation decreases after the lag to 0. Whereas the value of r can be determined as 0 and 1 refers to [4] . Thus, the value of r, s, b of each input series is obtained in Table 3.…”

Section: Identification (R S B) Transfer Function Modelmentioning

confidence: 99%

“…Based on significant of parameters and AIC scores, the transfer function model (1,0,4) is obtained for the air temperature input series and the transfer function model (0,0,6) for the wind speed input series. Thus, a single input transfer function model can be written in the following form:…”

Section: Estimating the Model Parameters Of A Single Input Transfer Fmentioning

confidence: 99%

“…The model of the single input transfer function obtained values (r, s, b) at air temperature is (1,0,4) and at wind speed values (r, s, b) are (0,0,6). Thus, the dual input transfer function model is obtained as follows:…”

Section: Determine the Dual Input Transfer Function Modelmentioning

confidence: 99%

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Forecasting rainfall using transfer function in Aceh province, Indonesia

Rusyana¹,

Salwa²,

Marziah³

2019

ams

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Aceh is a province located in the western Indonesia and Aceh has varied climate conditions. As one element of weather, rainfall is an important element in determining the weather. Components that affect rainfall include air temperature and wind speed. This study predicts rainfall based on its components with using transfer function. Transfer function is a model that mixes time series approach with causal approach.

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Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

Kunz

Dufek

et al. 2021

Batteries & Supercaps

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A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real‐world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2 % mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5 % of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real‐world conditions due to the impact of calendar degradation.

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Multivariate Time Series Analysis and Applications

Cited by 96 publications

References 0 publications

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Machine Learning to Forecast Medical Attentions of Pneumonia Cases in Colombian Cities: An implementation with Air Quality, Meteorological and Admission Data

Forecasting rainfall using transfer function in Aceh province, Indonesia

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

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