Markov model: Difference between revisions

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==Tolerant Markov model==

A Tolerant Markov model (TMM) is a probabilistic-algorithmic Markov chain model.<ref name="TMMs">{{cite book |first1=D. |last1=Pratas |first2=M. |last2=Hosseini |first3=A. J. |last3=Pinho |chapter=Substitutional tolerant Markov models for relative compression of DNA sequences |title=PACBB 2017 – 11th International Conference on Practical Applications of Computational Biology & Bioinformatics, Porto, Portugal |pages=265–272 |year=2017 |doi=10.1007/978-3-319-60816-7_32 |isbn=978-3-319-60815-0}}</ref> It assigns the probabilities according to a conditioning context that considers the last symbol, from the sequence to occur, as the most probable instead of the true occurring symbol. A TMM can model three different natures: substitutions, additions or deletions. Successful applications have been efficiently implemented in DNA sequences compression.<ref name="TMMs" /><ref name="GECO">{{cite book |first1=D. |last1=Pratas |first2=A. J. |last2=Pinho |first3=P. J. S. G. |last3=Ferreira |chapter-url=https://ieeexplore.ieee.org/abstract/document/7786167/ |chapter=Efficient compression of genomic sequences |title=Data Compression Conference (DCC), 2016 |pages=231–240 |publisher=IEEE |year=2016 |doi=10.1109/DCC.2016.60|isbn=978-1-5090-1853-6 }}</ref>

==Markov-chain forecasting models==

Markov-chains have been used as a forecasting methods for several topics, for example price trends<ref name="SLS">{{cite journal |first1=E.G. |last1=de Souza e Silva |first2=L.F.L. |last2=Legey |first3=E.A. |last3=de Souza e Silva |url=https://www.sciencedirect.com/science/article/pii/S0140988310001271 |title=Forecasting oil price trends using wavelets and hidden Markov models |journal=Energy Economics |volume=32 |year=2010}}</ref>, wind power<ref name="CGLT">{{cite journal |first1=A |last1=Carpinone |first2=M |last2=Giorgio |first3=R. |last3=Langella |first4=A. |last4=Testa |url=https://www.sciencedirect.com/science/article/pii/S0378779614004714 |title=Markov chain modeling for very-short-term wind power forecasting |journal=Electric Power Systems Research |volume=122 |pages=152–158 |year=2015|doi=10.1016/j.epsr.2014.12.025 }}</ref> and solar irradiance<ref name="MMW">{{cite journal |first1=J. |last1=Munkhammar |first2=D.W. |last2=van der Meer |first3=J. |last3=Widén |url=https://www.sciencedirect.com/science/article/pii/S0038092X19303469 |title=Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model |journal= Solar Energy |volume=184 |pages=688–695 |year=2019|doi=10.1016/j.solener.2019.04.014 }}</ref>. The Markov-chain forecasting models utilize a variety of different settings, from discretizing the time-series<ref name="CGLT" /> to hidden Markov-models combined with wavelets<ref name="SLS" /> and the Markov-chain mixture distribution model (MCM)<ref name="MMW" />.

== See also ==