Component Data Combination Forecasting Model and Its Application in Prediction and Analysis of Industrial Structure

DOI

10.2991/ammsa-18.2018.44How to use a DOI?

Keywords

logratio transformation; spherical coordinate transformation; combined forecasting model; industrial structure

Abstract

Composition data represent the relative information of data, not absolute information. The determination and restriction make the analysis of composition data different from other general data, and need to be transformed and re-analyzed first. For a series of chronological data collected, the paper uses two components of data formed by asymmetric logratio transformation and spherical coordinate transformation to establish the forecasting model respectively, and the two forecasting models are combined and forecasted to establish the forecasting model. According to the forecasting method, a forecasting model of Chinese fishery industrial structure is established and the future industrial structure of Chinese fishery is predicted.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lingrong Zhao
AU  - Ying Ping
AU  - Yuanhong Luo
PY  - 2018/05
DA  - 2018/05
TI  - Component Data Combination Forecasting Model and Its Application in Prediction and Analysis of Industrial Structure
BT  - Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)
PB  - Atlantis Press
SP  - 215
EP  - 219
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-18.2018.44
DO  - 10.2991/ammsa-18.2018.44
ID  - Zhao2018/05
ER  -