## 5 Experimental Results
### ANN
#### 5.5.2 Can News and Tweets Improve the Prediction of the Next Closing Price?
* Windowing This operator transforms a given example set containing series data into a new example set containing single valued examples. For this purpose, windows with a specified window and step size are moved across the series, and the attribute value lying horizon values after the window end is used as a label that should be predicted. In simpler words, we select the step in order to make the prediction. We have chosen to predict the next closing price based on the three previous days. * X-Validation ([[http://docs.rapidminer.com/studio/operators/validation/x_validation.html](http://docs.rapidminer.com/studio/operators/validation/x_validation.html)]([http://docs.rapidminer.com/studio/operators/validation/x_validation.html](http://docs.rapidminer.com/studio/operators/validation/x_validation.html))) This operator performs a cross-validation in order to estimate the statistical performance of a learning operator (usually on unseen datasets). It is mainly used to estimate how accurately a model (learned by a particular learning operator) will perform in practice. As previously explained, the two most accurate regression types were used for our experiments, i.e., linear regression and Support Vector Machines (SVM). As we can see in Figures 9-12, the improvement rates were better when we used the rules than the random intervals. Furthermore, using SVM regression, we have better results than with the linear regression. Similar results were found for the other four stocks, and in most cases, the rules improved the prediction of the next closing price. The improvements are depicted below in Tables 5 and 6. Figure 11: Improvement rates (expressed in %) at random intervals about AAPL by using linear regression. Figure 12: Improvement rates (expressed in %) at random intervals about AAPL by using SVM regression. Figure 10: Improvement rates (expressed in %) of pattern intervals (rules) about AAPL by using SVM regression. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sentiment and Tweets** & **Sentiment Only** & **Tweets Only** \\ \hline
**APPL Linear** & & & \\ \hline R\#2 & 0 & 0 & 0 \\ R\#5 & 0 & 0 & 2.93 \\ R\#6 & 11.13 & 30.39 & 6.36 \\ R\#7 & 0 & 0 & 0 \\ \hline
**APPL SVM** & & & \\ \hline R\#2 & 96.22 & 96.81 & 95.06 \\ R\#5 & 97.89 & 78.52 & 96.15 \\ R\#6 & 43.00 & 36.40 & 67.54 \\ R\#7 & 97.95 & 97.64 & 96.34 \\ \hline
**GE Linear** & & & \\ \hline R\#4 & 0.00 & 0.54 & 0.00 \\ R\#5 & 0.00 & 1.21 & 0.00 \\ R\#6 & 0.00 & 0.00 & 0.00 \\ R\#7 & 22.52 & 25.74 & 0.00 \\ \hline
**GE SVM** & & & \\ \hline R\#4 & 83.18 & 70.25 & 65.67 \\ R\#5 & 99.77 & 99.66 & 99.61 \\ R\#6 & 51.01 & 52.78 & 55.37 \\ R\#7 & 45.66 & 41.68 & 32.28 \\ \hline
**IBM Linear** & & & \\ \hline R\#4 & 0.00 & 0.89 & 0.00 \\ R\#5 & 33.41 & 1.00 & 37.61 \\ R\#6 & 0.00 & 0.00 & 0.00 \\ R\#7 & 0.00 & 0.00 & 0.00 \\ \hline
**IBM SVM** & & & \\ \hline R\#4 & 98.53 & 85.69 & 93.56 \\ R\#5 & 88.13 & 90.06 & 75.38 \\ R\#6 & 90.19 & 90.59 & 77.66 \\ R\#7 & 0.00 & 0.00 & 0.00 \\ \hline
**MSFT Linear** & & & \\ \hline R\#1 & 0.00 & 0.00 & 0.00 \\ R\#2 & 0.00 & 0.00 & 0.00 \\ R\#3 & 13.71 & 12.82 & 14.16 \\ R\#4 & 0.00 & 2.52 & 0.00 \\ \hline
**MSFT SVM** & & & \\ \hline R\#1 & 71.76 & 64.36 & 0.00 \\ R\#2 & 34.45 & 25.07 & 27.95 \\ R\#3 & 7.83 & 7.41 & 0.00 \\ R\#4 & 76.76 & 73.09 & 65.78 \\ \hline
**ORCL Linear** & & & \\ \hline R\#5 & 0.00 & 0.00 & 0.00 \\ R\#6 & 0.00 & 12.60 & 17.26 \\ R\#7 & 5.12 & 0.00 & 26.40 \\ \hline
**ORCL SVM** & & & \\ \hline R\#5 & 92.91 & 24.63 & 88.53 \\ R\#6 & 0.00 & 0.00 & 0.00 \\ R\#7 & 44.75 & 12.35 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The improvement rates of the next closing price of the five companies using rules (in RapidMiner).