**Bollinger Bands**

The Bollinger Band was introduce by John Bollinger in 1980s. These Bands depict the volatility of stock as it increases or decreases. The bands are placed above and below the moving average line of the stocks. The wider the gap between the bands, higher is the degree of volatility. On the other hand, as the width within the band decreases, lower is the degree of volatility of the stock. At times, the width within the band is constant over a period of time, which shows the constant behavior of a certain stock over that period of time.

There are three lines in the Bollinger Band,

• The middle line with N-period moving average (MA); 20-day SMA

• An upper band at K times an N-period standard deviation above the moving average; 20-day SMA + (20-day standard deviation of price x 2)

• A lower band at K times an N-period standard deviation below the moving average; 20-day SMA – (20-day standard deviation of price x 2) … **Graph Attention Network (GAT)**

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved state-of-the-art results across three established transductive and inductive graph benchmarks: the Cora and Citeseer citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs are entirely unseen during training). … **Information-Based Optimal Subdata Selection (IBOSS)**

Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large data sets due to computational limitations. A critical step in big data analysis is data reduction. Existing investigations in the context of linear regression focus on subsampling-based methods. However, not only is this approach prone to sampling errors, it also leads to a covariance matrix of the estimators that is typically bounded from below by a term that is of the order of the inverse of the subdata size. We propose a novel approach, termed information-based optimal subdata selection (IBOSS). Compared to leading existing subdata methods, the IBOSS approach has the following advantages: (i) it is significantly faster; (ii) it is suitable for distributed parallel computing; (iii) the variances of the slope parameter estimators converge to 0 as the full data size increases even if the subdata size is fixed, i.e., the convergence rate depends on the full data size; (iv) data analysis for IBOSS subdata is straightforward and the sampling distribution of an IBOSS estimator is easy to assess. Theoretical results and extensive simulations demonstrate that the IBOSS approach is superior to subsampling-based methods, sometimes by orders of magnitude. The advantages of the new approach are also illustrated through analysis of real data. …

# If you did not already know

**09**
*Monday*
Apr 2018

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