Collection of lecture on Markov Chains & Hidden Markov Models

The first set of lectures introduce markov model, markov chains, and extend the markov chain to HMMs

(ML 14.1) Markov models - motivating examples
http://www.youtube.com/watch?v=7KGdE2AK_MQ

(ML 14.2) Markov chains (discrete-time) (part 1)
http://www.youtube.com/watch?v=WUjt98HcHlk

(ML 14.3) Markov chains (discrete-time) (part 2)
http://www.youtube.com/watch?v=j6OUj9tleVM

(ML 14.4) Hidden Markov models (HMMs) (part 1)  
http://www.youtube.com/watch?v=TPRoLreU9lA

===========================================
The following lecture is a more detailed, intuitive explaination about HMMs

Very intuitive
http://www.youtube.com/watch?v=jY2E6ExLxaw

=============================================
 HMM & stock prediction
http://www.slideshare.net/ChiuYW/hidden-markov-model-stock-prediction

TOOL KIT

R Package– HMM– RHMM
JAVA– JHMM
Python– Scikit Learn

DEMO

GET DATASET

library(quantmod)
getSymbols("^TWII")
chartSeries(TWII)
TWII_Subset<- p="" start="as.Date(" window="">TWII_Train <- -="" cbind="" lose="" olume="" p="" pen="" twii_subset="" ubset="">

BUILD HMM MODEL

# Include RHMM Library
library(RHmm)

# Baum-Welch Algorithm
hm_model <- hmmfit="" nstates="5)</p" obs="TWII_Train">
# Viterbi Algorithm
VitPath <- hm_model="" p="" twii_train="" viterbi="">

SCATTER PLOT

TWII_Predict <- cbind="" lose="" p="" states="" ubset="" vitpath=""> chartSeries(TWII_Predict[,1])
addTA(TWII_Predict[TWII_Predict[,2]==1,1],on=1,type="p",col=5,pch=25)
addTA(TWII_Predict[TWII_Predict[,2]==2,1],on=1,type="p",col=6,pch=24)
addTA(TWII_Predict[TWII_Predict[,2]==3,1],on=1,type="p",col=7,pch=23)
addTA(TWII_Predict[TWII_Predict[,2]==4,1],on=1,type="p",col=8,pch=22)
addTA(TWII_Predict[TWII_Predict[,2]==5,1],on=1,type="p",col=10,pch=21)