Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance

计量经济学 消费(社会学) 计算机科学 气体消耗 天然气 环境科学 经济 工程类 环境经济学 社会学 社会科学 废物管理
作者
Nan Wei,Lihua Yin,Chao Li,Jinyuan Liu,Changjun Li,Yuanyuan Huang,Fanhua Zeng
出处
期刊:Energy [Elsevier BV]
卷期号:238: 122090-122090 被引量:20
标识
DOI:10.1016/j.energy.2021.122090
摘要

Data complexity has a great impact on daily natural gas consumption forecasting. However, due to the existence of irregular data, complex periodic change, and volatility data, the conventional methods, such as Lyapunov exponent and sample entropy, are failed to assess the complexity of the consumption data. Thus, this paper proposes a hybrid method of complexity measure, named CMLS. The novel method combined correlation coefficient analysis, missing data detect, Lyapunov exponent, and skewness analysis. Compared with Lyapunov exponent and sample entropy, CMLS is more stable and insensitive to the length of data in complexity measures. Additionally, for revealing the relationship between data complexity and forecasting performance, we design three case studies including 56 sets of daily natural gas consumption, and forecast with three advanced models. The results show that the forecasting performance various a lot in different complexity level. Particularly in very hard level, the daily natural gas consumption data is very hard to be forecasted and the R2 of forecasts are all negative. This paper serves as an initial study seeks to reveal the impact of data complexity on forecasting performance. The findings can help forecasters to evaluate the performance and difficulty of natural gas consumption forecasting.
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