Research on Time Series of Gas Trend Based on R Language

DOI

10.2991/ammsa-17.2017.75How to use a DOI?

Keywords

R language; time series; gas trend warning; simple exponential smoothing method; holt exponential smoothing method; holt-winters exponential smoothing method

Abstract

Use the history monitoring data of the gas of Shaanxi Huangling No.2 coal mine, the time series of monitoring data are decomposed and smoothed by using R language. The abnormal and missing values in the raw data are analyzed and three methods of time series smoothing (Simple exponential smoothing method, Holt exponential smoothing method and Holt-Winters exponential smoothing method) are used to predict the variation law of gas concentration. The actual value and the predicted value are compared to verify the effectiveness of the forecasting method, and the conclusion can make practical significance for the safe production of the mine.

Copyright

© 2017, 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  - Peng Wang
AU  - Xuewen Li
AU  - Xintan Chang
PY  - 2017/05
DA  - 2017/05
TI  - Research on Time Series of Gas Trend Based on R Language
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)
PB  - Atlantis Press
SP  - 336
EP  - 339
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-17.2017.75
DO  - 10.2991/ammsa-17.2017.75
ID  - Wang2017/05
ER  -