advanced-algorithmic-trading

221
15.3.1 Linear Regression
An elementary supervised technique from classical statistics that finds an optimal linear response
surface from a set of labelled predictor-response pairs.
15.3.2 Linear Classification
These supervised techniques classify data into groups, rather than predict numerical responses.
Common techniques include Logistic Regression, Linear Discriminant Analysis and Naive Bayes
Classification.
15.3.3 Tree-Based Methods
Decision trees are a supervised technique that partition the predictor/feature space into hypercubic
subsets. Ensembles of decision trees include Random Forests and Gradient Boosted
Regression Trees.
15.3.4 Support Vector Machines
SVMs are a supervised technique that attempts to create a linear separation boundary in a
higher-dimensional space than the original problem in order to deal with non-linear separation
of features.
15.3.5 Artificial Neural Networks and Deep Learning
Neural networks are a supervised technique that create hierarchies of activation "neurons" that
can approximate high-dimensional non-linear functions. "Deep" networks make use of many
hidden layers of neurons to form hierarchical representations for state-of-the-art classification
performance.
15.3.6 Bayesian Networks
Bayesian Networks or "Bayes Nets" are a type of probabilistic graphical model that represent
probabilistic relationships between variables. They are utilised both for inference and learning
applications.
15.3.7 Clustering
Clustering is an unsupervised technique that attempts to partition data into subsets according
to some similarity criteria. A common technique is K-Means Clustering.
15.3.8 Dimensionality Reduction
Dimensionality reduction algorithms are unsupervised techniques that attempt to transform the
space of predictors/factors into another set that explain the "variation" in the responses with
fewer dimensions. Principal Component Analysis is the canonical technique here.