Construction and Simulation of Economic Statistics Measurement Model Based on Time Series Analysis and Forecast

Lu Xu,Weijie Chen

doi:10.1155/2021/5963516

Abstract

Time series follow the basic principles of mathematical statistics and can provide a set of scientifically based dynamic data processing methods. Using this method, various types of data can be approximated by corresponding mathematical models, and then, the internal structure and complex characteristics of the data can be understood essentially, so as to achieve the purpose of predicting its development trend. This paper mainly studies the combined forecasting model based on the time series model and its application. First, the application prospects and research status of the combined forecasting model, the source of time series analysis, and the status of research development at home and abroad are given, and the purpose and significance of the research topic and the research content are summarized. Then, the paper gives the relevant theories about the ARIMA model and the basic principles of model recognition and explains the method of time series smoothing. Finally, the paper uses the ARIMA model to identify and fit the time series data and then the gray forecast model to fit and predict the time series data. Finally, by assigning reasonable weights and combining these methods, a combined forecasting model is proposed and carried out.

Highlights

Various things in the world are in motion, development, and change every day
We can control the error within a reliable range by adjusting model parameters and other means to ensure the validity of the results. is article mainly uses mean square error (MSE) to evaluate the accuracy of the prediction results
E statistical properties of the Autoregressive Moving Average Model (ARMA) (p, q) model are combined by the statistical properties of the Autoregressive model (AR) (p) model and the MA (q) model. erefore, the recognition problem of the ARMA (p, q) model is similar to that of the AR (p) model and MA (q). e relevance of the model as well as the special features of the ARMA (p, q) model is discussed in three cases

Summary

Introduction

Various things in the world are in motion, development, and change every day. In order to understand and grasp the laws of the operation and development of things or systems, people often make a series of observations in the order of time. Time series analysis is establishing a dynamic model to find the regularity of development and changes by analyzing, researching, searching, and simulating the observed time series [2]. It performs pattern recognition and parameter estimation and uses these as the research object to scientifically predict and control the future development trend [3]. With the in-depth research on the theory and application of time series [4], time series analysis has been compared with natural science fields such as computer networks, engineering technology, medical engineering, national economy, geophysics, signal processing, and mechanical vibration. Many aspects of the social sciences have a wide range of applications

Methods

Results

Conclusion