analyzing sequential data by hidden markov model (hmmhmm is statistic model which is widely used for data having continuation and extensibility such as time series stock market analysishealth checkupand speech recognition this section deals in detail with analyzing sequential data using hidden markov model (hmmhidden markov model (hmmhmm is stochastic model which is built upon the concept of markov chain based on the assumption that probability of future stats depends only on the current process state rather any state that preceded it for examplewhen tossing coinwe cannot say that the result of the fifth toss will be head this is because coin does not have any memory and the next result does not depend on the previous result mathematicallyhmm consists of the following variablesstates (sit is set of hidden or latent states present in hmm it is denoted by output symbols (oit is set of possible output symbols present in hmm it is denoted by state transition probability matrix (ait is the probability of making transition from one state to each of the other states it is denoted by observation emission probability matrix (bit is the probability of emitting/observing symbol at particular state it is denoted by