prior probability matrix (pit is the probability of starting at particular state from various states of the system it is denoted by hencea hmm may be defined as (soab)wheres { snis set of possible stateso { omis set of possible observation symbolsa is an nxn state transition probability matrix (tpm) is an nxm observation or emission probability matrix (epm) is an dimensional initial state probability distribution vector exampleanalysis of stock market data in this examplewe are going to analyze the data of stock marketstep by stepto get an idea about how the hmm works with sequential or time series data please note that we are implementing this example in python import the necessary packages as shown belowimport datetime import warnings nowuse the stock market data from the matpotlib finance packageas shown hereimport numpy as np from matplotlib import cmpyplot as plt from matplotlib dates import yearlocatormonthlocator tryfrom matplotlib finance import quotes_historical_yahoo_och except importerrorfrom matplotlib finance import quotes_historical_yahoo as quotes_historical_yahoo_och from hmmlearn hmm import gaussianhmm load the data from start date and end datei between two specific dates as shown herestart_date datetime date( end_date datetime date( quotes quotes_historical_yahoo_och ('intc'start_dateend_date