Data Science in Theory and Practice. Maria Cristina Mariani

Скачать книгу

The ‐plane and several parallel planes.

      Figure 16.3 The plane

.

      Figure 16.4 Two class problem when data is linearly separable.

      Figure 16.5 Two class problem when data is not linearly separable.

      Figure 16.6 ROC curve for linear SVM.

      Figure 16.7 ROC curve for nonlinear SVM.

      Figure 17.1 Single hidden layer feed‐forward neural networks.

      Figure 17.2 Simple recurrent neural network.

      Figure 17.3 Long short‐term memory unit.

      Figure 17.4 Philippines (PSI). (a) Basic RNN. (b) LTSM.

      Figure 17.5 Thailand (SETI). (a) Basic RNN. (b) LTSM.

      Figure 17.6 United States (NASDAQ). (a) Basic RNN. (b) LTSM.

      Figure 17.7 JPMorgan Chase & Co. (JPM). (a) Basic RNN. (b) LTSM.

      Figure 17.8 Walmart (WMT). (a) Basic RNN. (b) LTSM.

      Figure 18.1 3D power spectra of the daily returns from the four analyzed stock companies. (a) Discover. (b) Microsoft. (c) Walmart. (d) JPM Chase.

      Figure 18.2 3D power spectra of the returns (generated per minute) from the four analyzed stock companies. (a) Discover. (b) Microsoft. (c) Walmart. (d) JPM Chase.

      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

for volcanic eruptions 1 and 2.

      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

representation of market basket transaction data.

      Table 15.3 Grocery transactional data.

      Table 15.4 Transaction data.

      Table

Скачать книгу