| dc.description.abstract | Hidden Markov Models (MMM) is a stochastic model where the system being modeled is
assumed as a Markov process with unknown parameters. The challenge is to determine the
hidden parameters from observed parameters. It provides a probabilistic framework for
modeling a time series forecasting. Ifthe causative factors are not observed directly and has
the properties of Markov Chain, so, a pair of observation and its causative factors is Hidden
Markov Model. The proposed model was applied on minted gold price change in 2008 until
2010. Gold price was influenced by several factors, such as international recession, crude
price, government policy, etc. The algorithm used to estimate the parameters, thus the
estimated parameters were used to calculate the expectation value ofgold to found the most
likely sequence. The proposed model then would be coded using Matlab. The result of this
study showed that the continuous hidden Markov model could be applied on gold with
2.25%, 2.22%, 2.96% and 2.8% oftotal percentage error.
Keywords: Hidden MarkovModel, Time Series, Gold, Estimated Parameter | en_US |
