The Forecasting of Evolutionary Multi-Source Data limits

DOI

10.2991/icimm-16.2016.140How to use a DOI?

Keywords

Data Evolution, Dynamic Evolution Factor, Evolutionary Matrix, Limit Distribution

Abstract

This paper gives a method of forecasting data evolution in a long term, its practical significance is predicting data evolution direction and targeting the data that need to be updated.With the assumption of no absolutly real data in the world, we could view the data updating process as a data evolution in order to infinitely close to an objective entity. The EVO elements could be considered as the most close to real data in a specified period. In the long term, data evolution constitutes a Markov chain. According to Chapman-K Olmogorov Equation, we derive the Markov chains of data evolution in the long term. Furthermore ,we greatly simplifies the evolutionary computation by a properties of limiting distribution and the EVO elements. Experiments show that after a great times of updating , it still has a high prediction precision for the initial price.

Copyright

© 2016, 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  - Guoqi Chen
AU  - Fang Yu
PY  - 2016/11
DA  - 2016/11
TI  - The Forecasting of Evolutionary Multi-Source Data limits
BT  - Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 773
EP  - 777
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-16.2016.140
DO  - 10.2991/icimm-16.2016.140
ID  - Chen2016/11
ER  -