scholarly journals New statistical model for misreported data with application to current public health challenges

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David Moriña ◽  
Amanda Fernández-Fontelo ◽  
Alejandra Cabaña ◽  
Pedro Puig
Keyword(s):  
Public Health ◽  
Time Series ◽  
Human Papillomavirus ◽  
Continuous Time ◽  
Time Series Data ◽  
Real Data ◽  
Current Data ◽  
Series Data ◽  
Proposed Model ◽  
Public Health Challenges

AbstractThe main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

scholarly journals Modelling the dynamics of a public health care system: evidence from time-series data

2011 ◽  
Vol 44 (23) ◽  
pp. 2955-2968 ◽  
Author(s):  
Fabrizio Iacone ◽  
Steve Martin ◽  
Luigi Siciliani ◽  
Peter C. Smith
Keyword(s):  
Public Health ◽  
Health Care ◽  
Time Series ◽  
Health Care System ◽  
Time Series Data ◽  
Public Health Care ◽  
Series Data ◽  
Public Health Care System ◽  
Care System

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

Time Series Data Mining Implemented on Football Match

2010 ◽  
Vol 26-28 ◽  
pp. 98-103 ◽  
Author(s):  
Ben Cheng Chai
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Mining Technique ◽  
Time Series Data Mining ◽  
Proposed Model ◽  
Interesting Pattern ◽  
Football Match ◽  
The One

This study utilizes time series data mining to find the interesting pattern and cooperation custom. Meanwhile, data mining technique and some special football skills such as ball possession are employed to build a novel decision model in football match. The proposed model is expatiated through real football match. In short, on the one hand, the model provides a feasible route to guide the decision makers including football coach to establish effective mechanism in football match. On the other hand, it extends the application scope of time series data mining.

scholarly journals Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

2008 ◽  
Vol 5 (25) ◽  
pp. 885-897 ◽  
Author(s):  
Simon Cauchemez ◽  
Neil M Ferguson
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Continuous Time ◽  
Generation Time ◽  
Data Augmentation ◽  
Time Series Data ◽  
Observation Interval ◽  
Series Data ◽  
Time Model ◽  
Similar Time

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.

scholarly journals A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

2021 ◽  
Vol 7 ◽  
pp. e534
Author(s):  
Kristoko Dwi Hartomo ◽  
Yessica Nataliani
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Forecasting ◽  
Series Data ◽  
Improvement Rate ◽  
New Model ◽  
Average Improvement ◽  
Proposed Model

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

scholarly journals Time series forecasting of styrene price using a hybrid ARIMA and neural network model

2019 ◽  
Vol 10 (3) ◽  
pp. 915
Author(s):  
Ali Ebrahimi Ghahnavieh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Real Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Data Sets ◽  
Combined Model ◽  
Future Behavior ◽  
Hybrid Forecasting ◽  
Ann Models

Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.

Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Tae Hyong Kim ◽  
Ahnryul Choi ◽  
Hyun Mu Heo ◽  
Kyungran Kim ◽  
Kyungsuk Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Continuous Time ◽  
Classification Accuracy ◽  
Fall Detection ◽  
Series Data ◽  
Machine Learning Classifiers ◽  
Detection Model ◽  
Learning Classifiers ◽  
Proposed Model

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

Analysis of the attention to COVID-19 epidemic based on visibility graph network

2021 ◽  
pp. 2150316
Author(s):  
Qingxiang Feng ◽  
Haipeng Wei ◽  
Jun Hu ◽  
Wenzhe Xu ◽  
Fan Li ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Time Series Data ◽  
Search Behavior ◽  
Series Data ◽  
Global Public Health ◽  
Health Crisis ◽  
Time Space ◽  
Epidemic Period ◽  
Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

A quantum approach for time series data based on graph and Schrödinger equations methods

2018 ◽  
Vol 33 (35) ◽  
pp. 1850208 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Amandeep Kaur
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Error Rates ◽  
Series Data ◽  
Quantum Model ◽  
Effective Solution ◽  
Proposed Model ◽  
Quantum Approach ◽  
Speed Up ◽  
Competing Models

The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the chance of error rates. This paper introduces a new quantum model for time series data which depends on the appropriate length of intervals. To provide effective solution of this problem, this study suggests a new graph-based quantum approach. This technique is useful in discretization and representation of logical relationships. Then, we divide these logical relations into various groups to obtain efficient results. The proposed model is verified and validated with various approaches. Experimental results signify that the proposed model is more precise than existing competing models.

Sign in / Sign up

Export Citation Format

Share Document