Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction
Rainfall prediction is a challenging task due to its dependency on many natural phenomenon. Some authors used Hurst exponent as a predictability indicator to ensure predictability of the time series before prediction. In this paper, a detailed analysis has been done to ascertain whether a definite r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201820307002 |
| _version_ | 1819182278265274368 |
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| author | Sivapragasam Chandrasekaran Saravanan Poomalai Balamurali Saminathan Muttil Nitin |
| author_facet | Sivapragasam Chandrasekaran Saravanan Poomalai Balamurali Saminathan Muttil Nitin |
| author_sort | Sivapragasam Chandrasekaran |
| collection | DOAJ |
| description | Rainfall prediction is a challenging task due to its dependency on many natural phenomenon. Some authors used Hurst exponent as a predictability indicator to ensure predictability of the time series before prediction. In this paper, a detailed analysis has been done to ascertain whether a definite relation exists between a strong Hurst exponent and predictability. The one-lead monthly rainfall prediction has been done for 19 rain gauge station of the Yarra river basin in Victoria, Australia using Artificial Neural Network. The prediction error in terms of normalized Root Mean Squared Error has been compared with Hurst exponent. The study establishes the truth of the hypothesis for only 6 stations out of 19 stations, and thus recommends further investigation to prove the hypothesis. This concept is relevant for any time series which need to be used for real time process control. |
| first_indexed | 2024-12-22T22:43:35Z |
| format | Article |
| id | doaj.art-09d7bf80c93d41cab8b99f35b01c86be |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-22T22:43:35Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-09d7bf80c93d41cab8b99f35b01c86be2022-12-21T18:10:07ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012030700210.1051/matecconf/201820307002matecconf_iccoee2018_07002Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall PredictionSivapragasam ChandrasekaranSaravanan PoomalaiBalamurali SaminathanMuttil NitinRainfall prediction is a challenging task due to its dependency on many natural phenomenon. Some authors used Hurst exponent as a predictability indicator to ensure predictability of the time series before prediction. In this paper, a detailed analysis has been done to ascertain whether a definite relation exists between a strong Hurst exponent and predictability. The one-lead monthly rainfall prediction has been done for 19 rain gauge station of the Yarra river basin in Victoria, Australia using Artificial Neural Network. The prediction error in terms of normalized Root Mean Squared Error has been compared with Hurst exponent. The study establishes the truth of the hypothesis for only 6 stations out of 19 stations, and thus recommends further investigation to prove the hypothesis. This concept is relevant for any time series which need to be used for real time process control.https://doi.org/10.1051/matecconf/201820307002 |
| spellingShingle | Sivapragasam Chandrasekaran Saravanan Poomalai Balamurali Saminathan Muttil Nitin Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction MATEC Web of Conferences |
| title | Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction |
| title_full | Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction |
| title_fullStr | Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction |
| title_full_unstemmed | Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction |
| title_short | Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction |
| title_sort | ascertaining time series predictability in process control case study on rainfall prediction |
| url | https://doi.org/10.1051/matecconf/201820307002 |
| work_keys_str_mv | AT sivapragasamchandrasekaran ascertainingtimeseriespredictabilityinprocesscontrolcasestudyonrainfallprediction AT saravananpoomalai ascertainingtimeseriespredictabilityinprocesscontrolcasestudyonrainfallprediction AT balamuralisaminathan ascertainingtimeseriespredictabilityinprocesscontrolcasestudyonrainfallprediction AT muttilnitin ascertainingtimeseriespredictabilityinprocesscontrolcasestudyonrainfallprediction |