Point and Interval Estimation Method

목차

I. INTRODUCTION
II. MAXIMUM LIKELIHOOD OF PEARSON SYSTEM FOR AUTO-REGRESSIVE MODEL (MLPAR)
III. EXPERIMENT
IV. CONCLUSTION

본문내용

I. INTRODUCTION
There is an emphasis nowadays on the importance of big data as the amount of data increases. Big data are very large and complex data sets. For the most part, big data are continually measured with the flow of time, and this is known as time series data. Time series data are a sequence of data measured in various areas such as business-related fields, environment, and society [1]. The objective in analyzing time series data is to predict future data by using past data. For example, we can earn a profit by predicting stock prices or exchange rates in economics or prevent natural disasters by predicting precipitation or river outflows in environment science. Time series analysis also aids in making reasonable decisions with regard to social phenomena, security, and industrial production. Therefore, a study on decreasing the prediction error and the increasing accuracy in analyzing time series data is an important topic in a variety of fields.

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