Outlook Analysis of Future Discharge According to Land Cover Change Using CA-Markov Technique Based on GIS

2017

Abstract. In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM. Different methods for multiple categorical data sequences prediction has been proposed, e.g., the first-order multivariate Markov chain model, a more precise model named as higher-order multivariate Markov chain model has presented by W.K Ching in [8]. An improved multivariate Markov chain model has been proposed to speed up the convergence speed [10]. IntroductionIn this paper, we propose a tridiagonal parsimonious higher-order multivariate Markov chain model for improving the prediction precision and reducing the parameter number in the model. The organization of this paper is as follows. In Section 2, we review several definitions and models of Markov chain model. In Section 3, a tridiagonal parsimonious higher-order multivariate Markov chain model is proposed for multiple categorical data sequences. In Section 4, we estimate the parameters of the tridiagonal parsimonious higher-order multivariate Markov chain model. Numerical experiments show the effectiveness of our model in Section 5.

Section: Application To Sales Demand Prediction With Tridiagonal Parsmentioning

confidence: 99%

A tridiagonal parsimonious higher order multivariate Markov chain model

2017

“…We classifies the sales demand into six states (1,2,3,4,5,6), The customer's sales demand states in the same customer group of five important products of the company for a year is given in the From the results of Figure 1,2,3, we find that the simplified parsimonious higher-order multivariate Markov chain model preforms better than parsimonious higher-order multivariate Markov chain model and the higher-order multivariate Markov chain model in parameter number comparing, time consuming and the prediction precision.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Markov chains is an important implement in many research areas, such as, internet applications [2] music [3], software testing [4], land cover change [5], energy consumption [6], speech recognition [7], physics, gene expression [9], finance [10][11], DNA [12] and so on. It is helpful to develop a better model for a more accurate prediction.by exploring the relationships of different categorical data sequences is meaningful to accurate prediction.…”

Section: Introductionmentioning

confidence: 99%

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

2017

Abstract. In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC. Different methods for multiple categorical data sequences prediction (which means the relationships of different categorical data sequences are taken into account) has been proposed, e.g., the first-order multivariate Markov chain model, higher-order multivariate Markov chain model and an improved multivariate Markov chain model (to speed up the convergence) [10]. (They add a negative association part which is multiplied a constant for normalizing solutions at the back of the positive association part of the model. ). IntroductionThe organization of this article is organized as follows. In Section 2, we review some basic knowledge of Markov chain model. In Section 3, a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition is proposed for multiple categorical data sequences. In Section 4, we estimate the parameters of the simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. Numerical experiments show the effectiveness of our model in Section 5.

“…Markov chains is an important implement in many research areas, such as internet applications [2] music [3], software testing [4], land cover change [5], energy consumption [6], speech recognition [7], physics ,gene expression [9], finance [11], DNA [12] and so on. Exploring the relationships of different categorical data sequences for developing a model for more accurate prediction is meaningful research topic.…”

Section: Introductionmentioning

confidence: 99%

“…We classifies the sales demand into six states (1,2,3,4,5,6), e.g., 1= no sales volume, 2= vary low sales volume, 3= low sales volume, 4= standard sales volume, 5= fast sales volume, 6= vary fast sales volume. The customer's sales demand states in the same customer group of five important products of the company for a year is given in the Appendix [6].…”

mentioning

confidence: 99%

A simplified parsimonious higher order multivariate Markov chain model

2017

Abstract. In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM. Several prediction models of multiple categorical data sequences has been exhibited, e.g., the firstorder multivariate Markov chain model, the higher-order multivariate Markov chain model and the improved multivariate Markov chain model (which adds a negative association part at the back of the normal model.) IntroductionIn this article, a simplified parsimonious higher-order multivariate Markov chain model is proposed for a better the prediction results and less computational cost.The organization of this paper is as follows. In Section 2, we review several basic knowledge of Markov chain model. In Section 3, a simplified parsimonious higher-order multivariate Markov chain model is presented for different data sequences. In Section 4, the parameters estimation method of SPHOMMCM is given. Finally, numerical experiments illustrate the effectiveness of our model in Section 5.