Three main areas explored:

• Bayesian Statistics
  • Takes a different approach of probabiliy and considers it as a measure of belief. Opinions are used to create probability distributions.
• Time Series Analysis
  • Main idea of time series analysis is serial correlation (auto correlation). Refers to the statistical relationship between a time series and a lagged version of itself. It measures the degree to whih the values of a variable at different time points are correlated with each other.
• Machine Learning

Bayesian Statistics

Bayesian statistics provides us with mathematical tools to rationally update our subjective beliefs in light of new data or evidence

Concepts

• Markov Chain Monte Carlo
• Autoregressive Integrated Moving Average (ARIMA)
• Volatility clustering
• Condititional Heteroskedasticity
• Generalized Autoregressive Conditional Herosketastic (GARCH)
• Hidden Markov Models
• Decision Trees
• Support Vector Machines
• Random Forests
• K-Means Clustering