Figure 19.1 Time‐frequency image of explosion 1 recorded by ANMO (Table 19.2).
Figure 19.2 Time‐frequency image of earthquake 1 recorded by ANMO (Table 19.2).
Figure 19.3 Three‐dimensional graphic information of explosion 1 recorded by ANMO (Table 19.2).
Figure 19.4 Three‐dimensional graphic information of earthquake 1 recorded by ANMO (Table 19.2).
Figure 19.5 Time‐frequency image of explosion 2 recorded by TUC (Table 19.3).
Figure 19.6 Time‐frequency image of earthquake 2 recorded by TUC (Table 19.3).
Figure 19.7 Three‐dimensional graphic information of explosion 2 recorded by TUC (Table 19.3).
Figure 19.8 Three‐dimensional graphic information of earthquake 2 recorded by TUC (Table 19.3).
Figure 21.1
Figure 21.2 DFA for volcanic eruptions 1 and 2.
Figure 21.3 DEA for volcanic eruptions 1 and 2.
List of Tables
Table 2.1 Examples of random vectors.
Table 3.1 Ramus Bone Length at Four Ages for 20 Boys.
Table 4.1 Time series data of the volume of sales of over a six hour period.
Table 4.2 Simple moving average forecasts.
Table 4.3 Time series data used in Example 4.6.
Table 4.4 Weighted moving average forecasts.
Table 4.5 Trend projection of weighted moving average forecasts.
Table 4.6 Exponential smoothing forecasts of volume of sales.
Table 4.7 Exponential smoothing forecasts from Example 4.9.
Table 4.8 Adjusted exponential smoothing forecasts.
Table 6.1 Numbers.
Table 6.2 Files mode in Python.
Table 7.1 Common asymptotic notations.
Table 9.1 Temperature versus ice cream sales.
Table 12.1 Events information.
Table 12.2 Discriminant scores for earthquakes and explosions groups.
Table 12.3 Discriminant scores for Lehman Brothers collapse and Flash crash event.
Table 12.4 Discriminant scores for Citigroup in 2009 and IAG stock in 2011.
Table 13.1 Data matrix.
Table 13.2 Distance matrix.
Table 13.3 Stress and goodness of fit.
Table 13.4 Data matrix.
Table 14.1 Models' performances on the test dataset with 23 variables using AUC and mean square error (MSE) values for the five models.
Table 14.2 Top 10 variables selected by the Random forest algorithm.
Table 14.3 Performance for the four models using the top 10 features from model Random forest on the test dataset.
Table 15.1 Market basket transaction data.
Table 15.2 A binary
Table 15.3 Grocery transactional data.
Table 15.4 Transaction data.
Table 16.1 Models performances on the test dataset.
Table 18.1 Percentage of power for Discover data.
Table 18.2 Percentage of power for JPM data.
Table 18.3 Percentage of power for Microsoft data.
Table 18.4 Percentage of power for Walmart data.
Table 19.1 Determining
Table 19.2 Percentage of total power (energy) for Albuquerque, New Mexico (ANMO) seismic station.
Table 19.3 Percentage of total power (energy) for Tucson, Arizona (TUC) seismic station.
Table 21.1 Moments of the Poisson distribution with intensity
Table 21.2 Moments of the
Table 21.3 Scaling exponents of Volcanic Data time series.
Preface
This textbook is dedicated to practitioners, graduate, and advanced undergraduate students who have interest in Data Science, Business analytics, and Statistical and Mathematical Modeling in different disciplines such as Finance, Geophysics, and Engineering. This book is designed to serve as a textbook for several courses in the aforementioned areas and a reference guide for practitioners in the industry.
The book has a strong theoretical background and several applications to specific practical problems. It contains numerous techniques applicable to modern data science and other disciplines. In today's world, many fields are confronted with increasingly large amounts of complex data. Financial, healthcare, and geophysical data sampled with high frequency is no exception. These staggering amounts of data pose special challenges to the world of finance and other disciplines such as healthcare and geophysics, as traditional models and information technology tools can be poorly suited to grapple with their size and complexity. Probabilistic modeling, mathematical modeling, and statistical data analysis attempt to discover order from apparent disorder; this textbook may serve as a guide to various new systematic approaches on how to implement these quantitative activities with complex data sets.