Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting
Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical foreca...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1461 |
| الوسوم: |
إضافة وسم
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| _version_ | 1857415063941414912 |
|---|---|
| author | Panja, Madhurima |
| author2 | Chakraborty, Tanujit Kumar, Uttam Hadid, Abdenour |
| author2_role | author author author |
| author_facet | Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Hadid, Abdenour |
| author_role | author |
| dc.creator.none.fl_str_mv | Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Hadid, Abdenour |
| dc.date.none.fl_str_mv | 2023 2024-01-30T08:55:44Z 2024-01-30T08:55:44Z |
| dc.identifier.none.fl_str_mv | 978-981-99-8177-9 1865-0929 1865-0937 https://depot.sorbonne.ae/handle/20.500.12458/1461 10.1007/978-981-99-8178-6_35 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Communications in Computer and Information Science Neural Information Processing |
| dc.subject.none.fl_str_mv | Forecasting ARIMA Neural networks Hybrid model |
| dc.title.none.fl_str_mv | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedings |
| description | Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce a Probabilistic AutoRegressive Neural Network (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error. Notably, the PARNN model provides uncertainty quantification through prediction intervals and conformal predictions setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. The ability to quantify uncertainty through prediction intervals further enhances the model’s usefulness in various decision-making processes. |
| id | sorbonner_dcd801739efc44e67b0176e41cda4697 |
| identifier_str_mv | 978-981-99-8177-9 1865-0929 1865-0937 10.1007/978-981-99-8178-6_35 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1461 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range ForecastingPanja, MadhurimaChakraborty, TanujitKumar, UttamHadid, AbdenourForecastingARIMANeural networksHybrid modelForecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce a Probabilistic AutoRegressive Neural Network (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error. Notably, the PARNN model provides uncertainty quantification through prediction intervals and conformal predictions setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. The ability to quantify uncertainty through prediction intervals further enhances the model’s usefulness in various decision-making processes.2024-01-30T08:55:44Z2024-01-30T08:55:44Z2023Controlled Vocabulary for Resource Type Genres::text::conference object::conference proceedings978-981-99-8177-91865-09291865-0937https://depot.sorbonne.ae/handle/20.500.12458/146110.1007/978-981-99-8178-6_35enCommunications in Computer and Information ScienceNeural Information Processingoai:depot.sorbonne.ae:20.500.12458/14612024-03-07T14:46:28Z |
| spellingShingle | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting Panja, Madhurima Forecasting ARIMA Neural networks Hybrid model |
| title | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| title_full | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| title_fullStr | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| title_full_unstemmed | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| title_short | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| title_sort | Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting |
| topic | Forecasting ARIMA Neural networks Hybrid model |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1461 |