Time Series Data Mining: A Case Study With Big Data Analytics Approach

Fang Wang,Yiduo Mei,Wenrui Li,Menggang Li

doi:10.1109/access.2020.2966553

Abstract

Time series data is common in data sets has become one of the focuses of current research. The prediction of time series can be realized through the mining of time series data, so that we can obtain the development process and regularity of social economic phenomena reflected by time series, and extrapolate to predict its development trend. More and more attention has been paid to time series prediction in the era of big data. It is the basic application of time series prediction to accurately predict the trend. In this paper, we introduce various time series autoregressive (AR) model, moving average (MA) model, and ARIMA model that is combined by AR and MA. As the time series prediction in general scenarios, the ARIMA is applied to the risk prediction of the National SME Stock Trading (New Third Board) in combination with specific scenarios. The case studies show that the results of our analysis are basically consistent with the actual situation, which has greatly helped the prediction of financial risks.

Highlights

Time series data mining comes from the need of people to visualize data models according to their abilities
We mainly explore the AR and moving average (MA) prediction models, and explore the combination of the two models, ARIMA, which has a good method for processing non-stationary time series [6]
SUMMARY AND OUTLOOK With the continuous progress on time series data mining technology, its application has been extended to financial analysis and it can well predict the risks in the financial field in the future

Summary

INTRODUCTION

Time series data mining comes from the need of people to visualize data models according to their abilities. Time series prediction methods are divided into traditional time series prediction methods and machine learning methods. This method fits the historical time trend curve by establishing an appropriate mathematical model and predicts the trend of future time series according to the established model Curves, our common models include ARMA [2], VAR [3], TAR [4], ARCH [5], etc. The traditional time series method can be applied to a variety of scenarios because it relies on relatively simple data and only needs the historical time series trend curve to build a model. The final results are basically consistent with the actual results, and good prediction results have been obtained

RESEARCH BACKGROUND

NEW THIRD BOARD RISK FORECAST

ANALYTICAL METHOD

Findings

SUMMARY AND OUTLOOK