Network models of psychopathology and comorbidity: Philosophical and pragmatic considerations

2010 ◽  
Vol 33 (2-3) ◽  
pp. 159-160 ◽  
Author(s):  
S. Brian Hood ◽  
Benjamin J. Lovett
Keyword(s):  
Time Series ◽  
Individual Differences ◽  
Time Series Data ◽  
Network Models ◽  
Psychological Disorders ◽  
Series Data ◽  
Network Structures ◽  
Philosophical View ◽  
Individual Time Series ◽  
The Individual

AbstractCramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

2020 ◽  
Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Gene Regulatory

scholarly journals Identification of neutral biochemical network models from time series data

2009 ◽  
Vol 3 (1) ◽  
Author(s):  
Marco Vilela ◽  
Susana Vinga ◽  
Marco A Grivet Mattoso Maia ◽  
Eberhard O Voit ◽  
Jonas S Almeida
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Network Models ◽  
Biochemical Network ◽  
Series Data

scholarly journals Peramalan Data Runtun Waktu menggunakan Model Hybrid Time Series Regression – Autoregressive Integrated Moving Average

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Melisa Arumsari ◽  
◽  
Andrea Dani ◽  
Keyword(s):  
Time Series ◽  
Hybrid Method ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Regression ◽  
Accurate Forecast ◽  
Using Data ◽  
The Individual ◽  
Time Series Data Analysis

Forecasting is a method used to estimate or predict a value in the future using data from the past. With the development of methods in time series data analysis, a hybrid method was developed in which a combination of several models was carried out in order to produce a more accurate forecast. The purpose of this study was to determine whether the TSR-ARIMA hybrid method has a better level of accuracy than the individual TSR method so that more accurate forecasting results are obtained. The data in this study are monthly data on the number of passengers on American airlines for the period January 1949 to December 1960. Based on the analysis, the TSR-ARIMA hybrid method produces a MAPE of 3,061% and the TSR method produces an MAPE of 7,902%.

scholarly journals Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

2017 ◽  
Author(s):  
Sacha Epskamp ◽  
Claudia van Borkulo ◽  
Date C. van der Veen ◽  
Michelle Servaas ◽  
Adela-Maria Isvoranu ◽  
...  
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Recent Literature ◽  
Series Data ◽  
Network Structures ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Relevant Variables ◽  
Analysis Of Time Series

Recent literature has introduced (1) the network perspective to psychology, and (2) collection of time-series data in order to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intra-individual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time-series data. We explain the importance of partial correlation networks and exemplify the network structures on time-series data of a psychiatric patient.

scholarly journals Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object

2019 ◽  
Vol 9 (1) ◽  
pp. 314-320 ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Neural Network Models ◽  
Training Time ◽  
Sliding Windows ◽  
Learning Time

The prediction of time series data is a forecast using the analysis of a relationship pattern between what will be predicted (prediction) and the time variable. The prediction process using the recurrent neural network (RNN) model could recognize and learn the data pattern of time series, but the presence of fluctuations in data makes the introduction of data patterns difficult to be learned. The data used for forecasting are tourist visits to Tanah Lot Bali tourist attraction for 10 years (2008-2017). The training process uses the RNN method on high fluctuating data, which requires a relatively long time in recognizing and studying the data patterns. Modification of the RNN method on learning rate and momentum by using dynamic values, can shorten learning time. The results showed the learning time using the RNN dynamic value, smaller than the variants of the RNN method such as the RNN Elman, Jordan RNN, Fully RNN, LSTM and the feedforward method (Backpropagation). The resulting error value is 0,05105 MSE. This value is smaller than the Fully RNN, Jordan RNN, LSTM and Feedforward methods. The elman method has the shortest training time among other models. The purpose of this research is to make a prediction design consisting of sliding windows techniques, training with neural network models and validation of results with k-fold cross-validation.

scholarly journals MAPPING DISTURBANCE DYNAMICS IN WET SCLEROPHYLL FORESTS USING TIME SERIES LANDSAT

2016 ◽  
Vol XLI-B8 ◽  
pp. 633-641 ◽  
Author(s):  
A. Haywood ◽  
J. Verbesselt ◽  
P. J. Baker
Keyword(s):  
Time Series ◽  
Prescribed Burning ◽  
Time Series Data ◽  
Timber Harvesting ◽  
Series Data ◽  
Response Slope ◽  
Random Forest Models ◽  
Severity Class ◽  
Disturbance And Recovery ◽  
The Individual

In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat time series data. We analysed a representative State forest within the Central Highlands which has been exposed to a range of disturbances over the last 30 years, including timber harvesting (clearfell, selective and thinning) and fire (wildfire and prescribed burning). We fitted spectral time series models to annual normal burn ratio (NBR) and Tasseled Cap Indices (TCI), from which we extracted a range of disturbance and recovery metrics. With these metrics, three hierarchical random forest models were trained to 1) distinguish acute-severity disturbances from low-severity disturbances; 2a) attribute the disturbance agents most likely within the acute-severity class; 2b) and attribute the disturbance agents most likely within the low-severity class. Disturbance types (acute severity and low-severity) were successfully mapped with an overall accuracy of 72.9 %, and the individual disturbance types were successfully attributed with overall accuracies ranging from 53.2 % to 64.3 %. Low-severity disturbance agents were successfully mapped with an overall accuracy of 80.2 %, and individual agents were successfully attributed with overall accuracies ranging from 25.5 % to 95.1. Acute-severity disturbance agents were successfully mapped with an overall accuracy of 95.4 %, and individual agents were successfully attributed with overall accuracies ranging from 94.2 % to 95.2 %. Spectral metrics describing the disturbance magnitude were more important for distinguishing the disturbance agents than the post-disturbance response slope. Spectral changes associated with planned burning disturbances had generally lower magnitudes than selective harvesting. This study demonstrates the potential of landsat time series mapping for fire and timber harvesting disturbances at the agent level and highlights the need for distinguishing between agents to fully capture their impacts on ecosystem processes.

Tax reforms and revenue mobilization in Ghana

2016 ◽  
Vol 43 (4) ◽  
pp. 522-534 ◽  
Author(s):  
William Bekoe ◽  
Michael Danquah ◽  
Sampson Kwabena Senahey
Keyword(s):  
Time Series ◽  
Tax Reform ◽  
Time Series Data ◽  
Series Data ◽  
Historical Time ◽  
Tax Reforms ◽  
Tax System ◽  
Content Type ◽  
Reform Period ◽  
The Individual

Purpose The purpose of this paper is to comprehensively examine Ghana’s tax reform programme and investigate whether it has facilitated the revenue mobilization capacity of the overall tax system and of individual taxes on the basis of estimates of tax buoyancies and elasticities. Design/methodology/approach This study uses the proportional adjustment approach to estimate tax buoyancies and elasticities of the overall tax system and of individual taxes for the pre and post tax reform period over the 1970-2013 period. Findings The results show that in general, tax reforms had a positive influence on the overall tax structure and on the individual tax handles as evidenced in the more than unity buoyancy and elasticity. All the individual taxes, except excise duties, recorded buoyancies and elasticities of more than unity during the reform period. Practical implications Tax authorities ought to move away from income-based taxation which discriminates against saving and investment, in favour of consumption-based taxes in conformity with international standards. Emphasis must also be placed on those taxes that have high revenue elasticities. These taxes include the personal, corporate, the Value Added Tax, and the import duties. Originality/value In this study, the paper extends and disaggregates the data on taxes, account for discretionary tax changes from the historical time series data, and use the adjusted historical time series data to estimate tax elasticity. The study therefore provides an in-depth understanding of the effects of the tax reforms on the overall tax system and of individual taxes in Ghana.

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