A REVIEW OF PROBABILISTIC GRAPH MODELS FOR FEATURE SELECTION WITH APPLICATIONS IN ECONOMIC AND FINANCIAL TIME SERIES FORECASTING

In every field of life, people are interested to be able to forecast future.  A number of techniques are available to predict and forecasting upto a certain level of accuracy. Many techniques involve statistical tools and techniques for forecasting, modeling and control. Use of statistical techniques is growing with time and new techniques are being developed very rapidly. Especially in the field of economics and finance, the estimation and forecasting of economic and financial indicators play a vital role in decision making. Many models are developed in the last 2 decades to get better accuracy and efficiency in time series analysis and still there is a scope of learning and getting betterment in this field is available. In this research we have reviewed probability graphs, directed acyclic graphs, Bayesian networks, feature selection algorithms and Markov blankets for time series forecasting on the economic and financial problems (like stock exchange forecasting, multi-objective business risk analysis, consumers’ analysis, portfolio optimization, credit scoring etc). This is a new dimension for adaptive modeling techniques in economics and finance modeling

Process variation aware system-level task allocation using stochastic ordering of delay distributions

Abstract — Design variability due to within-die and die-to-die variations has potential to significantly reduce the maximum operating frequency and effective performance of the system in future process technology generations. When multiple cores in MPSoC have different delay distri-butions, the problem of assigning tasks to the cores become challenging. This paper targets system level task allocation to stochastically minimize the total execution time of an application on MPSoC under process variation. In this work, we first introduce stochastically optimal task allocation problem. We provide formal theorems of the optimality of the solution in simple scenarios. We extend our theoretical work for generic cases in normal distribution. The proposed techniques enable efficient computation of task allocation using non-stochastic analysis. We apply these techniques in allocating tasks in the embedded system benchmark suites on MPSoC. We show that deterministic solution for system-level task allocation on widely used benchmark topologies and distributions (normal distribution) is almost as good as the best probabilistic solution. I