论时间序列数据结构变化的游程

THE 2ND INTERNATIONAL CONFERENCE ON SCIENCE, MATHEMATICS, ENVIRONMENT, AND EDUCATION Pub Date : 2019-12-18 DOI:10.1063/1.5139819

Wiwik Prihartanti, Dwilaksana Abdullah Rasyid, Nur Iriawan

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引用次数: 1

摘要

在整个记录序列数据的时间序列中，数据变化的运动往往不能被看作是一个单一的模型。在时间序列数据建模中，必须适应模型结构经常变化的情况。本文旨在研究马尔可夫转换模型在捕捉三家公司(PT. Indofood Sukses Makmur Tbk)收盘价股票数据结构变化中的应用。(INDF.JK)， PT. Indofood CBP suks Makmur Tbk。(ICBP.JK)和PT. Mustika Ratu Tbk。(MRAT.JK))从LQ45的成员和非成员中，估计每个模型结构的运行长度，并提前一步预测股票。利用期望最大化方法对马尔可夫切换模型中的参数进行估计。结果表明，三家公司都有一个以上的结构模型。最好的投资股票是IDNF。拥有最长平均运行长度(ARL)的JK股提供了更大的预测机制和股票价值的概率。在整个记录序列数据的时间序列中，数据变化的运动往往不能被看作是一个单一的模型。在时间序列数据建模中，必须适应模型结构经常变化的情况。本文旨在研究马尔可夫转换模型在捕捉三家公司(PT. Indofood Sukses Makmur Tbk)收盘价股票数据结构变化中的应用。(INDF.JK)， PT. Indofood CBP suks Makmur Tbk。(ICBP.JK)和PT. Mustika Ratu Tbk。(MRAT.JK))从LQ45的成员和非成员中，估计每个模型结构的运行长度，并提前一步预测股票。利用期望最大化方法对马尔可夫切换模型中的参数进行估计。结果表明，三家公司都有一个以上的结构模型。最好的投资股票是IDNF。拥有最长平均运行长度(ARL)的JK股提供了更大的预测机制和股票价值的概率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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On the run-length of the structural change in time series data

The movement of data changes in time series often cannot be seen as a single model throughout the time the serial data is recorded. The occurrence of model structure changes often must be accommodated in time series data modeling. This paper aims to study the Markov Switching models in capturing the structural changes the closing price stocks data of three companies (PT. Indofood Sukses Makmur Tbk. (INDF.JK), PT. Indofood CBP Sukses Makmur Tbk. (ICBP.JK), and PT. Mustika Ratu Tbk. (MRAT.JK)) from the member and not members of LQ45, estimating the run-length for each model structure, and forecasting the one-step-ahead of stocks. The parameters in the Markov Switching models are estimated using the Expectation Maximization (EM). The result shows that the three companies had more than one structural model. The best stock for investment is IDNF.JK share which have the longest Average Run Length (ARL) that provides the bigger probability of the forecasting regime and the stock values.The movement of data changes in time series often cannot be seen as a single model throughout the time the serial data is recorded. The occurrence of model structure changes often must be accommodated in time series data modeling. This paper aims to study the Markov Switching models in capturing the structural changes the closing price stocks data of three companies (PT. Indofood Sukses Makmur Tbk. (INDF.JK), PT. Indofood CBP Sukses Makmur Tbk. (ICBP.JK), and PT. Mustika Ratu Tbk. (MRAT.JK)) from the member and not members of LQ45, estimating the run-length for each model structure, and forecasting the one-step-ahead of stocks. The parameters in the Markov Switching models are estimated using the Expectation Maximization (EM). The result shows that the three companies had more than one structural model. The best stock for investment is IDNF.JK share which have the longest Average Run Length (ARL) that provides the bigger probability of the forecasting regime and the stock values.

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THE 2ND INTERNATIONAL CONFERENCE ON SCIENCE, MATHEMATICS, ENVIRONMENT, AND EDUCATION

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