A Novel Approach towards the Analysis of Stochastic High Frequency Data Analysis using ARIMA Model

Abstract

Conventionally, ARIMA (Autoregressive Integrated Moving Average) has been one of the most accepted, used and fundamental model in field of time series analysis and prediction. However, the ARIMA is a parametric model that cannot easily capture the nonlinear nature. In spite of these deficiencies, it has been used and applied in the terms of time series analysis and prediction to set bench mark for further and advanced comparative analysis and prediction. Maximum number of the available reviewed paper and chapter has its own kind of limits as they concentrates on the particular application of financial market or explores machine learning tools and techniques that was applied on the particular dataset. This study will provide a comparative study of some relevant existing tools and the techniques applied in the area of the financial market analysis. The main aim of this study is: (i) a comparative study of the recent available model of the area, (ii) Provide a generalized modeling for the stock market analysis and prediction (iii) review of the fundamental and futuristic challenges of the field (iv) Realization of the model by the use of fundamental and generalized ARIMA model.