scholarly journals Unveiling endogeneity and temporal dependence in energy prices and demand in Iberian countries: a stochastic hidden Markov model approach

2021 ◽  
Author(s):  
Jorge Antunes ◽  
Luis Alberiko Gil-Alana ◽  
Rossana Riccardi ◽  
Yong Tan ◽  
Peter Wanke
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Policy Implications ◽  
Time Dependent ◽  
Energy Prices ◽  
Temporal Dependence ◽  
Spot Prices ◽  
Daily Data ◽  
Energy Behavior

AbstractIn this paper, we analyze the temporal dependence in energy prices and demand using daily data of Portugal and Spain over the period 2007–2017. The methodology used is based on a stochastic Hidden Markov Model and the results indicate first that all significant relationships between energy prices and demands were found to be positive; second, spot prices are only time dependent on future prices and spot energy, while future energy is solely time dependent on spot energy behavior; third, future prices are not only autocorrelated but also time-dependent with spot energy and future energy demands level; and finally, spot energy is autocorrelated and time-dependent with future prices and future energy. Policy implications of the results obtained are presented at the end of the article.

scholarly journals Unveiling endogeneity and temporal dependence between tourism revenues/expenditures and macroeconomic variables in Brazil: A stochastic hidden Markov model approach

2018 ◽  
Vol 25 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Peter Wanke ◽  
Otávio Henrique dos Santos Figueiredo ◽  
Jorge Junio Moreira Antunes
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Policy Implications ◽  
Gdp Growth ◽  
Past Performance ◽  
Macroeconomic Variables ◽  
Tourism Activity ◽  
Tourism Revenue ◽  
Model Approach

Thus far, a comprehensive analysis on the feedback processes that may exist between macroeconomic variables and tourism activity in Brazil is still missing. This article fills this literature gap by analyzing the endogenous and temporally dependent pattern between Brazilian monthly tourism revenue/expenditures and macroeconomic variables over 20 years. A novel stochastic hidden Markov model approach reveals the feedback processes that exist between them. While tourism revenues are autocorrelated and impacted by gross domestic product (GDP) growth, tourism expenditures are detached from any macroeconomic variable, but are rather directly dependent on tourism revenues past performance, which also exert an impact on exchange rates and GDP growth, thus indirectly benefiting tourism expenditures abroad. Policy implications in terms of a specific tourism exchange rate for Brazil are derived in order to sustain tourism expenditures apart from tourism revenue flows.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions

Implementation of Android Based Speech Recognition for Indonesian Geography Dictionary

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Spoken Word ◽  
Silent Condition ◽  
Word Detection ◽  
Index Terms ◽  
To Receive ◽  
Noisy Condition

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.

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