Literatura académica sobre el tema "Multi-step ahead forecasting"
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Artículos de revistas sobre el tema "Multi-step ahead forecasting"
Kaboudan, M. A. "WAVELETS IN MULTI-STEP-AHEAD FORECASTING". IFAC Proceedings Volumes 38, n.º 1 (2005): 36–41. http://dx.doi.org/10.3182/20050703-6-cz-1902.02242.
Texto completoFindley, D. F. "Model Selection for Multi-Step-Ahead Forecasting". IFAC Proceedings Volumes 18, n.º 5 (julio de 1985): 1039–44. http://dx.doi.org/10.1016/s1474-6670(17)60699-2.
Texto completoHayder, Gasim, Mahmud Iwan Solihin y M. R. N. Najwa. "Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM". H2Open Journal 5, n.º 1 (25 de enero de 2022): 43–60. http://dx.doi.org/10.2166/h2oj.2022.134.
Texto completoCHANG, FI-JOHN, YEN-MING CHIANG y LI-CHIU CHANG. "Multi-step-ahead neural networks for flood forecasting". Hydrological Sciences Journal 52, n.º 1 (febrero de 2007): 114–30. http://dx.doi.org/10.1623/hysj.52.1.114.
Texto completoMcElroy, Tucker S. y David F. Findley. "Selection between models through multi-step-ahead forecasting". Journal of Statistical Planning and Inference 140, n.º 12 (diciembre de 2010): 3655–75. http://dx.doi.org/10.1016/j.jspi.2010.04.032.
Texto completoXiong, Tao, Yukun Bao y Zhongyi Hu. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices". Energy Economics 40 (noviembre de 2013): 405–15. http://dx.doi.org/10.1016/j.eneco.2013.07.028.
Texto completoXiong, Shenghua, Chunfeng Wang, Zhenming Fang y Dan Ma. "Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm". Energies 12, n.º 1 (2 de enero de 2019): 147. http://dx.doi.org/10.3390/en12010147.
Texto completoLi, Fang, Lihua Zhang, Xiao Wang y Shihu Liu. "Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series". Journal of Intelligent & Fuzzy Systems 42, n.º 3 (2 de febrero de 2022): 2023–39. http://dx.doi.org/10.3233/jifs-211405.
Texto completoSuradhaniwar, Saurabh, Soumyashree Kar, Surya S. Durbha y Adinarayana Jagarlapudi. "Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies". Sensors 21, n.º 7 (1 de abril de 2021): 2430. http://dx.doi.org/10.3390/s21072430.
Texto completoSu, Haokun, Xiangang Peng, Hanyu Liu, Huan Quan, Kaitong Wu y Zhiwen Chen. "Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network". Mathematics 10, n.º 14 (6 de julio de 2022): 2366. http://dx.doi.org/10.3390/math10142366.
Texto completoTesis sobre el tema "Multi-step ahead forecasting"
Leon, Ojeda Luis. "Short-term multi-step ahead traffic forecasting". Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT081/document.
Texto completoThis dissertation falls within the domain of the Intelligent Transportation Systems (ITS). In particular, it is concerned with the design of a methodology for the real-time multi-step ahead travel time forecasting using flow and speed measurements from a instrumented freeway. To achieve this objective this thesis develops two main methodologies. The first one, a model-free, uses only speed measurements collected from the freeway, where a mean speed is assumed between two consecutive collection points. The travel time is forecasted using a noise Adaptive Kalman Filter (AKF) approach. The process noise statistics are computed using an online unbiased estimator, while the observations and their noise statistics are computed using the clustered historical traffic data. Forecasting problems are reformulated as filtering ones through the use of pseudo-observations built from historical data. The second one, a model-based, uses mainly traffic flow measurements. Its main appealing is the use of a mathematical model in order to reconstruct the internal state (density) in small road portions, and consequently exploits the relation between density and speed to forecast the travel time. The methodology uses only boundary conditions as inputs to a switched Luenberger state observer, based on the ``Cell Transmission Model'' (CTM), to estimate the road initial states. The boundary conditions are then forecasted using the AKF developed above. Consequently, the CTM model is run using the initial conditions and the forecasted boundaries in order to obtain the future evolution of densities, speeds, and finally travel time. The added innovation in this approach is the space discretization achieved: indeed, portions of the road, called ``cells'', can be chosen as small as desired and thus allow obtaining a finer tracking of speed variations. In order to validate experimentally the developed methodologies, this thesis uses as study case the Grenoble South Ring. This freeway, enclosing the southern part of the city from A41 to A480, consists of two carriageways with two lanes. For this study only the direction east-west was considered. With a length of about 10.5 km, this direction has 10 on-ramps, 7 off-ramps, and is monitored through the Grenoble Traffic Lab (GTL) that is able to provide reliable traffic data every 15 s, which makes it possible for the forecasting strategies to be validated in real-time. The results show that both methods present strong capabilities for travel time forecasting: considering the entire freeway, in 90% of the cases it was obtained a maximum forecasting error of 25% up to a forecasting horizon of 45 min. Furthermore, both methods perform as good as, or better than, the average historical. In particular, it is obtained that for horizons larger than 45 min, the forecasting depended exclusively on the historical data. For the dataset considered, the assessment study also showed that the model-based approach was more suitable for horizons shorter than 30 min
Ben, Taieb Souhaib. "Machine learning strategies for multi-step-ahead time series forecasting". Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209234.
Texto completoHistorically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.
The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.
Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.
In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.
Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.
We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.
Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.
Doctorat en sciences, Spécialisation Informatique
info:eu-repo/semantics/nonPublished
Pathirana, Vindya Kumari. "Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance". Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5757.
Texto completoCapítulos de libros sobre el tema "Multi-step ahead forecasting"
Barba Maggi, Lida Mercedes. "Multi-Step Ahead Forecasting". En Multiscale Forecasting Models, 49–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94992-5_3.
Texto completoDzalbs, Ivars y Tatiana Kalganova. "Multi-step Ahead Forecasting Using Cartesian Genetic Programming". En Inspired by Nature, 235–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67997-6_11.
Texto completoHajizadeh, Ehsan y Amin Hajizadeh. "Multi-step Ahead Power Demand Forecasting in Smart Grid". En Handbook of Smart Energy Systems, 1–9. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72322-4_97-1.
Texto completoChang, F. J., Y. C. Lo, P. A. Chen, L. C. Chang y M. C. Shieh. "Multi-Step-Ahead Reservoir Inflow Forecasting by Artificial Intelligence Techniques". En Knowledge-Based Information Systems in Practice, 235–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13545-8_14.
Texto completoNourani, Vahid, Parnian Ghaneei y Elnaz Sharghi. "Multi-Step-Ahead Forecasting of Groundwater Level Using Model Ensemble Technique". En Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications, 247–57. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2948-9_24.
Texto completoShao, Haijian, Chunlong Hu, Xing Deng y Dengbiao Jiang. "Multi-step Ahead Time Series Forecasting Based on the Improved Process Neural Networks". En Proceedings of the 9th International Conference on Computer Engineering and Networks, 397–404. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3753-0_38.
Texto completoKoprinska, Irena, Mashud Rana y Ashfaqur Rahman. "Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting". En Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, 436–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30490-4_35.
Texto completoDe Stefani, Jacopo, Olivier Caelen, Dalila Hattab, Yann-Aël Le Borgne y Gianluca Bontempi. "A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting". En ECML PKDD 2018 Workshops, 7–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13463-1_1.
Texto completoThu, Nguyen Thi Hoai, Pham Nang Van y Phan Quoc Bao. "Multi-step Ahead Wind Speed Forecasting Based on a Bi-LSTM Network Combined with Decomposition Technique". En Computational Intelligence Methods for Green Technology and Sustainable Development, 569–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19694-2_50.
Texto completoDabrowski, Joel Janek, YiFan Zhang y Ashfaqur Rahman. "ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-step-Ahead Time-Series Forecasting". En Neural Information Processing, 579–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_48.
Texto completoActas de conferencias sobre el tema "Multi-step ahead forecasting"
Garcia-Vega, Sergio, German Castellanos-Dominguez, Michel Verleysen y John A. Lee. "Multi-step-ahead forecasting using kernel adaptive filtering". En 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727463.
Texto completoGupta, Priya y Rhythm Singh. "Univariate model for hour ahead multi-step solar irradiance forecasting". En 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). IEEE, 2021. http://dx.doi.org/10.1109/pvsc43889.2021.9519002.
Texto completoPeralta Donate, Juan, Paulo Cortez, Araceli Sanchis de Miguel y German Gutierrez Sanchez. "Evolving sparsely connected neural networks for multi-step ahead forecasting". En the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2001982.
Texto completoHong, Chao-Fu, Yung-Sheng Liao, Mu-Hua Lin y Tsai-Hsia Hong. "A Study of Improving the Coherence in Multi-Step Ahead Forecasting". En 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.152.
Texto completoCao, Lili, Huajing Fang y Xiaoyong Liu. "Multi-step ahead forecasting for fault prognosis using Hidden Markov Model". En 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162191.
Texto completoda Silva, Ramon Gomes, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, Viviana Mariani y Leandro Coelho. "WIND ENERGY MULTI-STEP AHEAD FORECASTING BASED ON VARIATIONAL MODE DECOMPOSITION". En 18th Brazilian Congress of Thermal Sciences and Engineering. ABCM, 2020. http://dx.doi.org/10.26678/abcm.encit2020.cit20-0400.
Texto completoFu, Yiwei, Wei Hu, Maolin Tang, Rui Yu y Baisi Liu. "Multi-step Ahead Wind Power Forecasting Based on Recurrent Neural Networks". En 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2018. http://dx.doi.org/10.1109/appeec.2018.8566471.
Texto completoBonetto, Riccardo y Michele Rossi. "Parallel multi-step ahead power demand forecasting through NAR neural networks". En 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2016. http://dx.doi.org/10.1109/smartgridcomm.2016.7778780.
Texto completoKaur, Devinder, Shama Naz Islam y Md Apel Mahmud. "A Bayesian Probabilistic Technique for Multi-Step Ahead Renewable Generation Forecasting". En 2021 IEEE 2nd International Conference on Smart Technologies for Power, Energy and Control (STPEC). IEEE, 2021. http://dx.doi.org/10.1109/stpec52385.2021.9718767.
Texto completoDuong, Hieu N., Vu H. Nguyen, Tam M. Nguyen, Hien T. Nguyen y Vaclav Snasel. "The Hybrid Approaches for Forecasting Real Time Multi-step-ahead Boiler Efficiency". En IMCOM '16: The 10th International Conference on Ubiquitous Information Management and Communication. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2857546.2857563.
Texto completoInformes sobre el tema "Multi-step ahead forecasting"
McCracken, Michael W. y Tucker McElroy. Multi-Step Ahead Forecasting of Vector Time Series. Federal Reserve Bank of St. Louis, 2012. http://dx.doi.org/10.20955/wp.2012.060.
Texto completo