Chapter 11. Probabilistic Reasoning for Sequential Data
In this chapter, we are going to learn how to build sequence learning models. We will learn how to handle time-series data in Pandas. We will understand how to slice time-series data and perform various operations on it. We will discuss how to extract various stats from time-series data on a rolling basis. We will learn about Hidden Markov Models and then implement a system to build those models. We will understand how to use Conditional Random Fields to analyze sequences of alphabets. We will discuss how to analyze stock market data using the techniques learnt so far.
By the end of this chapter, you will learn about:
- Handling time-series data with Pandas
- Slicing time-series data
- Operating on time-series data
- Extracting statistics from time-series data
- Generating data using Hidden Markov Models
- Identifying alphabet sequences with Conditional Random Fields
- Stock market analysis
