Time Series Predicting

In the previous chapters, we discussed Hidden Markov Models (HMMs) and various algorithms associated with inference in great theoretical detail. From this chapter onward, we will be discussing the use of HMMs.

HMMs are capable of predicting and analyzing time-based phenomena. Because of this, they can be used in fields such as speech recognition, natural language processing, and financial market prediction. In this chapter, we will be looking into applications of HMMs in the field of financial market analysis, mainly stock price prediction.

Stock price prediction using HMM

Stock market prediction has been one of the more active research areas in the past, given the obvious interest of a lot of major companies. Historically, various machine learning algorithms have been applied with varying degrees of success. However, stock forecasting is still severely limited due to its non-stationary, seasonal, and unpredictable nature. Predicting forecasts from just the previous stock data is an even more challenging task since it ignores several outlying factors.

As seen previously, HMMs are capable of modeling hidden state transitions from the sequential observed data. The problem of stock prediction can also be thought as following the same pattern. The price of the stock depends upon a multitude of factors which generally remain invisible to the investor (hidden variables). The transition between the underlaying factors change...

Summary

In this chapter, we predicted the price of stocks using HMM. We applied the parameter-estimation and evaluation-of-model methods to determine the closing price of a stocks. Using HMM in stock market analysis is just another example of the application of HMM in analyzing time series data.

In the next chapter, we will look at an interesting application of HMM in the field of natural language processing.