A denoising method based on the nonlinear relationship between the target variable and input features
Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, ChunYu [verfasserIn] Lan, Qiujun [verfasserIn] Mi, Xiaoting [verfasserIn] Zhou, Zhongding [verfasserIn] Ma, Chaoqun [verfasserIn] Mi, Xianhua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 218 |
|---|---|
| Übergeordnetes Werk: |
volume:218 |
| DOI / URN: |
10.1016/j.eswa.2023.119585 |
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| Katalog-ID: |
ELV009233296 |
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| 520 | |a Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. | ||
| 650 | 4 | |a Time series denoising | |
| 650 | 4 | |a Empirical mode decomposition | |
| 650 | 4 | |a Maximum information coefficient | |
| 650 | 4 | |a Stock trend prediction | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Lan, Qiujun |e verfasserin |0 (orcid)0000-0001-7523-9487 |4 aut | |
| 700 | 1 | |a Mi, Xiaoting |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Zhongding |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Chaoqun |e verfasserin |0 (orcid)0000-0001-5387-7357 |4 aut | |
| 700 | 1 | |a Mi, Xianhua |e verfasserin |0 (orcid)0000-0003-2351-2255 |4 aut | |
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10.1016/j.eswa.2023.119585 doi (DE-627)ELV009233296 (ELSEVIER)S0957-4174(23)00086-6 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, ChunYu verfasserin aut A denoising method based on the nonlinear relationship between the target variable and input features 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. Time series denoising Empirical mode decomposition Maximum information coefficient Stock trend prediction Machine learning Lan, Qiujun verfasserin (orcid)0000-0001-7523-9487 aut Mi, Xiaoting verfasserin aut Zhou, Zhongding verfasserin aut Ma, Chaoqun verfasserin (orcid)0000-0001-5387-7357 aut Mi, Xianhua verfasserin (orcid)0000-0003-2351-2255 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 218 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:218 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 218 |
| spelling |
10.1016/j.eswa.2023.119585 doi (DE-627)ELV009233296 (ELSEVIER)S0957-4174(23)00086-6 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, ChunYu verfasserin aut A denoising method based on the nonlinear relationship between the target variable and input features 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. Time series denoising Empirical mode decomposition Maximum information coefficient Stock trend prediction Machine learning Lan, Qiujun verfasserin (orcid)0000-0001-7523-9487 aut Mi, Xiaoting verfasserin aut Zhou, Zhongding verfasserin aut Ma, Chaoqun verfasserin (orcid)0000-0001-5387-7357 aut Mi, Xianhua verfasserin (orcid)0000-0003-2351-2255 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 218 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:218 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 218 |
| allfields_unstemmed |
10.1016/j.eswa.2023.119585 doi (DE-627)ELV009233296 (ELSEVIER)S0957-4174(23)00086-6 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, ChunYu verfasserin aut A denoising method based on the nonlinear relationship between the target variable and input features 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. Time series denoising Empirical mode decomposition Maximum information coefficient Stock trend prediction Machine learning Lan, Qiujun verfasserin (orcid)0000-0001-7523-9487 aut Mi, Xiaoting verfasserin aut Zhou, Zhongding verfasserin aut Ma, Chaoqun verfasserin (orcid)0000-0001-5387-7357 aut Mi, Xianhua verfasserin (orcid)0000-0003-2351-2255 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 218 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:218 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 218 |
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10.1016/j.eswa.2023.119585 doi (DE-627)ELV009233296 (ELSEVIER)S0957-4174(23)00086-6 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, ChunYu verfasserin aut A denoising method based on the nonlinear relationship between the target variable and input features 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. Time series denoising Empirical mode decomposition Maximum information coefficient Stock trend prediction Machine learning Lan, Qiujun verfasserin (orcid)0000-0001-7523-9487 aut Mi, Xiaoting verfasserin aut Zhou, Zhongding verfasserin aut Ma, Chaoqun verfasserin (orcid)0000-0001-5387-7357 aut Mi, Xianhua verfasserin (orcid)0000-0003-2351-2255 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 218 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:218 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 218 |
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10.1016/j.eswa.2023.119585 doi (DE-627)ELV009233296 (ELSEVIER)S0957-4174(23)00086-6 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Zhang, ChunYu verfasserin aut A denoising method based on the nonlinear relationship between the target variable and input features 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. Time series denoising Empirical mode decomposition Maximum information coefficient Stock trend prediction Machine learning Lan, Qiujun verfasserin (orcid)0000-0001-7523-9487 aut Mi, Xiaoting verfasserin aut Zhou, Zhongding verfasserin aut Ma, Chaoqun verfasserin (orcid)0000-0001-5387-7357 aut Mi, Xianhua verfasserin (orcid)0000-0003-2351-2255 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 218 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:218 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 218 |
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Zhang, ChunYu @@aut@@ Lan, Qiujun @@aut@@ Mi, Xiaoting @@aut@@ Zhou, Zhongding @@aut@@ Ma, Chaoqun @@aut@@ Mi, Xianhua @@aut@@ |
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Zhang, ChunYu |
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004 DE-600 54.72 bkl A denoising method based on the nonlinear relationship between the target variable and input features Time series denoising Empirical mode decomposition Maximum information coefficient Stock trend prediction Machine learning |
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ddc 004 bkl 54.72 misc Time series denoising misc Empirical mode decomposition misc Maximum information coefficient misc Stock trend prediction misc Machine learning |
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ddc 004 bkl 54.72 misc Time series denoising misc Empirical mode decomposition misc Maximum information coefficient misc Stock trend prediction misc Machine learning |
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a denoising method based on the nonlinear relationship between the target variable and input features |
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A denoising method based on the nonlinear relationship between the target variable and input features |
| abstract |
Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. |
| abstractGer |
Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. |
| abstract_unstemmed |
Increasing the accuracy of prediction models in financial markets is an important but difficult task due to the natural complexities of financial time series, which are nonlinear and nonstationary. This challenge has made machine learning methods popular in recent years. However, the noise contained in financial series dramatically distorts the performance of such approaches. This paper proposes an adaptive denoising method called MIC-EMD that is data-driven and removes the noise contained in input features based on the nonlinear relationship between the target variable and the input features. To verify the advantages of MIC-EMD, a simulation experiment is conducted to compare the performance of several representative denoising methods with that of MIC-EMD. Finally, a comprehensive empirical analysis is performed for the trend predictions of six major indexes in Asian markets using three prevalent machine learning methods (SVM, random forest and LightBGM). After the input features are denoised by MIC-EMD, the results reveal the following: (i) its prediction performance outperforms that of the three learning models with input features denoised by state-of-the-art denoising methods such as ICA, WT, WF, EMD, aEMD, P-EMD and S-EMD, e.g., the prediction accuracies of the three machine learning models increase by 9.77%, 13.5% and 12.3%; and (ii) we obtain a prediction accuracy as high as 70%. |
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| score |
7.3972692 |