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.

A Continuous-Time, Latent-Variable Model of Time Series Data

2015 ◽  
Vol 23 (2) ◽  
pp. 278-298 ◽  
Author(s):  
Alexander M. Tahk
Keyword(s):  
Time Series ◽  
Continuous Time ◽  
Latent Variable ◽  
Time Series Data ◽  
Series Data ◽  
Continuous Time Model ◽  
Variable Model ◽  
Time Model ◽  
Vector Autoregressive ◽  
Irregular Spacing

Many types of time series data in political science, including polling data and events data, exhibit important features'such as irregular spacing, noninstantaneous observation, overlapping observation times, and sampling or other measurement error'that are ignored in most statistical analyses because of model limitations. Ignoring these properties can lead not only to biased coefficients but also to incorrect inference about the direction of causality. This article develops a continuous-time model to overcome these limitations. This new model treats observations as noisy samples collected over an interval of time and can be viewed as a generalization of the vector autoregressive model. Monte Carlo simulations and two empirical examples demonstrate the importance of modeling these features of the data.

A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

Short-term wind power forecasts by a synthetical similar time series data mining method

2018 ◽  
Vol 115 ◽  
pp. 575-584 ◽  
Author(s):  
Gaiping Sun ◽  
Chuanwen Jiang ◽  
Pan Cheng ◽  
Yangyang Liu ◽  
Xu Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Wind Power ◽  
Time Series Data ◽  
Series Data ◽  
Mining Method ◽  
Short Term ◽  
Similar Time ◽  
Time Series Data Mining ◽  
Data Mining Method

Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data

2017 ◽  
Vol 44 (9) ◽  
pp. 954-965 ◽  
Author(s):  
Jihoon Moon ◽  
Jinwoong Park ◽  
Sanghoon Han ◽  
Eenjun Hwang
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Time Series Data ◽  
Series Data ◽  
Educational Institutions ◽  
Similar Time

scholarly journals Effect of time-series length and resolution on abundance- and trait-based early warning signals of population declines

2019 ◽  
Author(s):  
A.A. Arkilanian ◽  
C.F. Clements ◽  
A. Ozgul ◽  
G. Baruah
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Generation Time ◽  
Time Series Data ◽  
Series Data ◽  
Series Length ◽  
Warning Signals ◽  
Relative Time ◽  
Early Warning Signals ◽  
Population Collapse

AbstractNatural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS’ performance decreases with decreasing length and resolution. Our simulations suggest a relative time-series length between ten and five generations and a resolution of half a generation are the minimum requirements for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths and resolutions half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

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