Olson D.L., Wu D. Predictive Data Mining Models

Springer, 2017. — 102 p. — Computational Risk Management — ISBN: 978-9811025426This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book’s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.Knowledge Management
Computer Support Systems
Examples of Knowledge Management
Data Mining Forecasting ApplicationsData Sets
Gold
Brent Crude
Stock IndicesBasic Forecasting Tools
Moving Average Models
Regression Models
Time Series Error Metrics
Seasonality
Daily Data
Change in Daily Price
Software DemonstrationsMultiple Regression
Data Series
Correlation
LagsRegression Tree Models
R Regression Trees
WEKA Regression Trees
Random ForestsAutoregressive Models
ARIMA Models
GARCH Models
Regime Switching ModelsClassification Tools
Bankruptcy Data Set
Logistic Regression
Support Vector Machines
Neural Networks
Decision Trees
Random Forests
Boosting
Full Data
ComparisonPredictive Models and Big Data