Hidden Markov Model and Forward-Backward Algorithm in Crude Oil Price Forecasting

Bon, Abdul Talib; Isah, Nuhu

doi:10.1088/1757-899x/160/1/012067

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…HMM is a double stochastic process that introduces the hidden states with the unobservable underlying stochastic process. HMM has been applied to time-series forecasting in different domains such as forecasting prices of stock and option [36][37][38], commodity market [39], electricity [40,41], crude oil [42], and currency exchange rate [43] from 2007 to 2018. As the real-life market is complex and highly volatile with nonlinear relationships, HMM helps model the time-series data with the hidden states.…”

Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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“…Selain itu, HMM juga dapat mendeteksi berbagai kegiatan dalam dunia bisnis, seperti fraud [13], perkiraan harga minyak mentah [14], dan prediksi peluang kenaikan harga saham [15].…”

Section: Pendahuluanunclassified

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

Jollyta

Oktarina

Johan³

2020

JEPIN

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Teknologi pengenal suara (speech recognition) merupakan teknologi yang berkembang pesat dalam bidang kecerdasan buatan (artificial intelligent). Saat ini, teknologi pengenal suara menjadi hal yang komersil melalui berbagai media teknologi seperti smartphone dan komputer. Salah satu pembentuk struktur pengenal suara agar dapat bekerja pada perangkat tersebut adalah model statistik pengenal suara Hidden Markov Model (HMM). Penerapan HMM pada berbagai kasus menunjukkan bahwa model ini cocok dengan berbagai macam data. Tulisan ini merupakan sebuah tinjauan untuk model HMM yang bertujuan untuk memberikan gambaran dan pemahaman terhadap kinerja HMM melalui rangkuman sejumlah penelitian yang digunakan dalam berbagai data. Penerapan HMM tersebut menunjukkan optimalisasi kinerja HMM dan tinjauan terhadap sejumlah penelitian menunjukkan bahwa tingkat keberhasilan HMM dalam mengenali data mencapai 71.43%.

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“…Among the main reason for this interest is the economy development of many countries is significantly affected by oil price volatility [12]. Many theories predict that crude oil price volatility can reduced investment because of the fear of incurring lost [2]. Similarly, the fluctuation is the main key in pricing crude oil and the major determinant of the value.…”

Section: Introductionmentioning

confidence: 99%

“…It is well know that fluctuation in crude oil prices influences economic health of a country, corporate financial lives, and our personal. An 'intelligent' model for forecasting the crude oil price would be attracted a great of attention [2].…”

Section: Introductionmentioning

confidence: 99%

A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

2019

IJITEE

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Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

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Hidden Markov Model and Forward-Backward Algorithm in Crude Oil Price Forecasting

Cited by 8 publications

References 7 publications

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Tinjauan Kasus Model Speech Recognition: Hidden Markov Model

A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

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