Abstract
The concept of big data has become one of the most important topics in the field of information science and engineering. In this paper, we offer modeling of data and its stability and forecasting by considering anti-symmetric traceless and symmetric models for atmospheric pressure variations. The data sample has been collected every 10 minutes for several years during 2009-2016 at the Weather Station, Max Planck Institute for Biogeochemistry, Jena, Germany. Subsequently, we extend the proposed model with a probabilistic transformation matrix by considering the Google search random surfer matrix with a small damping factor . Following the Principal Component Analysis (PCA), our study plays a vital role in big data samples and their stability analysis. A comparative discussion is provided for the above transformation matrix and its probabilistic counterpart. Finally, predictions are made towards feature selection, PCA and data compression sensing in the light of big data.
Highlights
Forecasting is an important problem concerning the weather and its modeling [1]
Our focus is towards the study of big data samples
We consider that a big data sample is large and it has complex data structures
Summary
Forecasting is an important problem concerning the weather and its modeling [1]. Following the same, in this paper, we provide modeling of data samples in the light of their stability and forecasting. The modeling of data and its stability are important in predicting the future behavior of configuration In this direction, we have studied the Richadson Integration over fluctuations of its step sizes for arbitrary real valued integratable functions [5]. A trust region approaches are used to determine the second order methods to non-convex problems In such methods, in order to remove a negative curvature, the Hessian techniques are defined as a damped configuration. In the light of forecasting and data analysis, Google search random surfers are used as a matrix for various probabilistic transformations. A Google surfer matrix generates the best optimal prediction by using a large matrix of a considered data with reference to their connectivity as outlined .
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