Hybrid Time Series Method for Long-Time Temperature Series Analysis
This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/9968022 |
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| _version_ | 1832561358162362368 |
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| author | Guangdong Huang Jiahong Li |
| author_facet | Guangdong Huang Jiahong Li |
| author_sort | Guangdong Huang |
| collection | DOAJ |
| description | This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well. |
| format | Article |
| id | doaj-art-60128b9297884142980f63b51c704bd6 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-60128b9297884142980f63b51c704bd62025-02-03T01:25:09ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/99680229968022Hybrid Time Series Method for Long-Time Temperature Series AnalysisGuangdong Huang0Jiahong Li1School of Science, China University of Geosciences, Beijing 100083, ChinaSchool of Economics and Management, Beihang University, Beijing 100191, ChinaThis paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well.http://dx.doi.org/10.1155/2021/9968022 |
| spellingShingle | Guangdong Huang Jiahong Li Hybrid Time Series Method for Long-Time Temperature Series Analysis Discrete Dynamics in Nature and Society |
| title | Hybrid Time Series Method for Long-Time Temperature Series Analysis |
| title_full | Hybrid Time Series Method for Long-Time Temperature Series Analysis |
| title_fullStr | Hybrid Time Series Method for Long-Time Temperature Series Analysis |
| title_full_unstemmed | Hybrid Time Series Method for Long-Time Temperature Series Analysis |
| title_short | Hybrid Time Series Method for Long-Time Temperature Series Analysis |
| title_sort | hybrid time series method for long time temperature series analysis |
| url | http://dx.doi.org/10.1155/2021/9968022 |
| work_keys_str_mv | AT guangdonghuang hybridtimeseriesmethodforlongtimetemperatureseriesanalysis AT jiahongli hybridtimeseriesmethodforlongtimetemperatureseriesanalysis |