China 
Africa 
Explore chapters and articles related to this topic
Cross-Recurrence Quantification Analysis for Distinguishing Emotions Induced by Indian Classical Music
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
M. Sushrutha Bharadwaj, V. G. Sangam, Shantala Hegde, Anand Prem Rajan
Biomedical signals are represented as time series, which is a series of data samples indexed in the time order. Time series is a data sequence recorded at successive equally spaced points in time. Analysis of time series comprises extraction of useful statistics and other features of the data (Galka 2000). There are several tools and techniques used to analyze time series. The methods used to analyze time series may be divided into distinct types based on the domain, parameters, linearity or the number of variables used for analysis. Since brain is a nonlinear dynamical system, nonlinear and chaotic analysis tools would be more useful to analyze the time series and the underlying system (Diks 1999).
Structural Equation Modeling with Longitudinal Data
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
Another type of longitudinal model that can be used for the same salt intake and blood pressure example is termed an autoregressive model. Here when we refer to an autoregressive model (other fields use this term differently), we mean a time series model that regresses current time point values on values from previous time points for that same time series. A time series is a sequence of data points recorded at intervals, usually regularly, over a period of time. Time series analysis, which can be useful for data collected in a longitudinal study, is often focused on assessing the time-dependency across the data points.
Fundamentals
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Medical practitioners are interested in finding an embedded pattern in time-series data, trend analysis, variations after an event such as medication-administration and the effect of medications. Such investigation pertains to chronic disease management where multiple time-stamped data show some pattern that can be correlated to medication or environmental conditions or dietary inputs. They are also interested in knowing the time-interval of an event. This form of abstraction requires the integration of aggregating multiple time-stamped data samples to a time-interval and intelligent inferencing techniques described in Chapter 3.
Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models
Published in Journal of Applied Statistics, 2023
An ARIMA13]. In particular, an ARIMAd = 1 has been used in the rest of this paper. Thus we only need to describe ARMA models here. If the series has a strong and consistent seasonal pattern, then we should use a seasonal difference to deal with the data. In time series analysis, the analysis of such data requires the use of seasonal ARIMA model. The seasonal part of an ARIMA
Traffic violation analysis using time series, clustering and panel zero-truncated one-inflated mixed model
Published in International Journal of Injury Control and Safety Promotion, 2022
Zahra Rezaei Ghahroodi, Samaneh Eftekhari Mahabadi, Sara Bourbour, Helia Safarkhanloo, Shokoufa Zeynali
The monthly number of violations aggregated over all vehicles, to be analyzed both as a univariate time series and also assuming location wise data as a spatial time series. Time series constitute a series of data points collected or sampled at fixed intervals. Monthly violation counts for a certain period of time also constitute a time series data. The time series data of traffic violations is very important to study as it can reveal the future trend of violations such as the time periods that it tends to increase or decrease. Also, spatial analysis allows for future violation predictions in different locations so that preventive measures can be taken. Generally, forecasting the future trend of traffic violation, can help control traffic violation and identify the black spot of traffic violation (Button, 2014; Jiaxing et al., 2010).
Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting
Published in Journal of Applied Statistics, 2021
Asiye Nur Yildirim, Eren Bas, Erol Egrioglu
Artificial neural networks (ANNs), one of the commonly used artificial intelligence methods; are based on mathematical modelling of the learning process inspired by the human brain. ANNs are the structures formed by artificial neural neurons coming together. In general, ANNs have three layers: the input layer, hidden layer and the output layer. All layers have a significant effect on the performance of the network and especially the hidden layer has special importance since its number cannot be determined precisely. Based on this problem, Yadav et al. [34] proposed the single multiplicative neuron model artificial neural networks (SMNM-ANN). In the studies about SMNM-ANN, the model obtained from the network is only a single model and this situation can be accepted when time series is stationary. But a time series is not always stationary. It is not possible to encounter a stationary time series in real life. The time series include some components such as trend or/and seasonality. It is not realistic to explain and analyse this type of time series with a single model. Besides, using a single model assumption may have misleading results.