Artículos de revistas: "Multi-step ahead forecasting"

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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.

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Findley, 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.

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Hayder, 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.

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Abstract Kelantan river (Sungai Kelantan in Malaysia) basin is one of the essential catchments as it has a history of flood events. Numerous studies have been conducted in river basin modelling for the prediction of flow and mitigation of flooding events as well as water resource management. Therefore, having multi-step-ahead forecasting for river flow (RF) is of important research interest in this regard. This study presents four different approaches for multi-step-ahead forecasting for the Kelantan RF, using NARX (nonlinear autoregressive with exogenous inputs) neural networks and deep learning recurrent neural networks called LSTM (long short-term memory). The dataset used was obtained in monthly record for 29 years between January 1988 and December 2016. The results show that two recursive methods using NARX and LSTM are able to do multi-step-ahead forecasting on 52 series of test datasets with NSE (Nash–Sutcliffe efficiency coefficient) values of 0.44 and 0.59 for NARX and LSTM, respectively. For few-step-ahead forecasting, LSTM with direct sequence-to-sequence produces promising results with a good NSE value of 0.75 (in case of two-step-ahead forecasting). However, it needs a larger data size to have better performance in longer-step-ahead forecasting. Compared with other studies, the data used in this study is much smaller.

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CHANG, 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.

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McElroy, 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.

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Xiong, 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.

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7

Xiong, 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.

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The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

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Li, 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.

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In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis.

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Suradhaniwar, 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.

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High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.

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Su, 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.

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Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.

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Duan, Jiuding y Hisashi Kashima. "Learning to Rank for Multi-Step Ahead Time-Series Forecasting". IEEE Access 9 (2021): 49372–86. http://dx.doi.org/10.1109/access.2021.3068895.

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Luna, Ivette, Ieda G. Hidalgo, Paulo S. M. Pedro, Paulo S. F. Barbosa, Alberto L. Francato y Paulo B. Correia. "FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING". Pesquisa Operacional 37, n.º 1 (enero de 2017): 129–44. http://dx.doi.org/10.1590/0101-7438.2017.037.01.0129.

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13

Ben Taieb, Souhaib, Antti Sorjamaa y Gianluca Bontempi. "Multiple-output modeling for multi-step-ahead time series forecasting". Neurocomputing 73, n.º 10-12 (junio de 2010): 1950–57. http://dx.doi.org/10.1016/j.neucom.2009.11.030.

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14

Ahmed, Adil y Muhammad Khalid. "Multi-step Ahead Wind Forecasting Using Nonlinear Autoregressive Neural Networks". Energy Procedia 134 (octubre de 2017): 192–204. http://dx.doi.org/10.1016/j.egypro.2017.09.609.

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15

Huck, Nicolas. "Pairs trading and outranking: The multi-step-ahead forecasting case". European Journal of Operational Research 207, n.º 3 (diciembre de 2010): 1702–16. http://dx.doi.org/10.1016/j.ejor.2010.06.043.

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16

Roy, Dilip Kumar, Tapash Kumar Sarkar, Sheikh Shamshul Alam Kamar, Torsha Goswami, Md Abdul Muktadir, Hussein M. Al-Ghobari, Abed Alataway, Ahmed Z. Dewidar, Ahmed A. El-Shafei y Mohamed A. Mattar. "Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models". Agronomy 12, n.º 3 (27 de febrero de 2022): 594. http://dx.doi.org/10.3390/agronomy12030594.

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Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station.

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Zhou, Yanlai, Fi-John Chang, Li-Chiu Chang, I.-Feng Kao, Yi-Shin Wang y Che-Chia Kang. "Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting". Science of The Total Environment 651 (febrero de 2019): 230–40. http://dx.doi.org/10.1016/j.scitotenv.2018.09.111.

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Wang, Dong Feng, Fu Qiang Wang y Pu Han. "Multi-Step-Ahead Forecasting of Wind Speed Based on EMD-RBF Model". Advanced Materials Research 347-353 (octubre de 2011): 2219–22. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2219.

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Wind speed forecasting is critical for wind energy conversion systems. Adaptive and reliable methods and techniques of wind speed forecasting are urgently needed in view of the stochastic nature of wind resource, which is varying from time to time and from site to site. Multi-step-ahead speed forecasting is built with empirical mode decomposition (EMD) method and RBF neural network, which makes use of non-linear and non-stationary signal characteristics. Time series of original wind speed data is decomposed by EMD method. And RBF neural network is used to predict the decomposition of the various components. Experimental results show that the method effectively improves the accuracy and the reliability of wind speed forecasting.

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Chen, Yanhui, Yingchao Zou, Yuzhen Zhou y Chuan Zhang. "Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method". Procedia Computer Science 91 (2016): 1050–56. http://dx.doi.org/10.1016/j.procs.2016.07.147.

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Du, Pei, Jianzhou Wang, Wendong Yang y Tong Niu. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system". Renewable Energy 122 (julio de 2018): 533–50. http://dx.doi.org/10.1016/j.renene.2018.01.113.

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Feng, Bin, Jianmin Xu, Yonggang Zhang y Yongjie Lin. "Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network". Applied Sciences 11, n.º 10 (13 de mayo de 2021): 4423. http://dx.doi.org/10.3390/app11104423.

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Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.

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Rodríguez, Nibaldo, Claudio Cubillos y José-Miguel Rubio. "Multi-Step-Ahead Forecasting Model for Monthly Anchovy Catches Based on Wavelet Analysis". Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/798464.

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This paper presents ap-step-ahead forecasting strategy based on two stages to improve pelagic fish-catch time-series modeling by considering annual and interannual fluctuations for northern Chile (18°S–24°S). In the first stage, the stationary wavelet transform is used to separate the raw time series into an annual component and an interannual component, whereas the periodicities of each component are obtained using the Morlet wavelet power spectrum. In the second stage, a linear autoregressive model is constructed to predict each component and the unknownp-next values are forecasted by the addition of the two predicted components. We demonstrate the utility of the proposed forecasting model on monthly anchovy-catches time series for periods from January 1963 to December 2007. Empirical results obtained for 10-month-ahead forecasting showed the effectiveness of the proposed wavelet autoregressive strategy.

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Zhan, Xingbin, Shuaichao Zhang, Wai Yuen Szeto y Xiqun (Michael) Chen. "Multi-step-ahead traffic speed forecasting using multi-output gradient boosting regression tree". Journal of Intelligent Transportation Systems 24, n.º 2 (18 de marzo de 2019): 125–41. http://dx.doi.org/10.1080/15472450.2019.1582950.

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Wang, Yun, Zongxia Xie, Qinghua Hu y Shenghua Xiong. "Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning". Energy Conversion and Management 163 (mayo de 2018): 384–406. http://dx.doi.org/10.1016/j.enconman.2018.02.034.

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Vassallo, Daniel, Raghavendra Krishnamurthy, Thomas Sherman y Harindra J. S. Fernando. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting". Energies 13, n.º 20 (20 de octubre de 2020): 5488. http://dx.doi.org/10.3390/en13205488.

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Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ∼8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.

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Rodriguez, Nibaldo, Gabriel Bravo y Lida Barba. "Haar Wavelet Neural Network for Multi-step-ahead Anchovy Catches Forecasting". Polibits 50 (31 de julio de 2014): 49–53. http://dx.doi.org/10.17562/pb-50-7.

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Yamamura, Mariko y Isao Shoji. "A nonparametric method of multi-step ahead forecasting in diffusion processes". Physica A: Statistical Mechanics and its Applications 389, n.º 12 (junio de 2010): 2408–15. http://dx.doi.org/10.1016/j.physa.2010.02.018.

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Ferreira, Lucas Borges y Fernando França da Cunha. "Multi-step ahead forecasting of daily reference evapotranspiration using deep learning". Computers and Electronics in Agriculture 178 (noviembre de 2020): 105728. http://dx.doi.org/10.1016/j.compag.2020.105728.

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Kley-Holsteg, Jens y Florian Ziel. "Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso". Journal of Water Resources Planning and Management 146, n.º 10 (octubre de 2020): 04020077. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001268.

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Wang, Yun, Tuo Chen, Shengchao Zhou, Fan Zhang, Ruming Zou y Qinghua Hu. "An improved Wavenet network for multi-step-ahead wind energy forecasting". Energy Conversion and Management 278 (febrero de 2023): 116709. http://dx.doi.org/10.1016/j.enconman.2023.116709.

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Aslam, Muhammad, Jun-Sung Kim y Jaesung Jung. "Multi-step ahead wind power forecasting based on dual-attention mechanism". Energy Reports 9 (diciembre de 2023): 239–51. http://dx.doi.org/10.1016/j.egyr.2022.11.167.

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Lorek, Kenneth S. y G. Lee Willinger. "Multi-Step-Ahead Quarterly Cash-Flow Prediction Models". Accounting Horizons 25, n.º 1 (1 de marzo de 2011): 71–86. http://dx.doi.org/10.2308/acch.2011.25.1.71.

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SYNOPSIS: We provide new empirical evidence supportive of the Brown-Rozeff ARIMA model as a candidate univariate statistically based expectation model for multi-period-ahead projections of quarterly cash flows. It provides 1- through 20-step-ahead projections of quarterly cash flows that are significantly more accurate than those generated by the premier multivariate quarterly time-series, disaggregated-accrual regression model popularized by Lorek and Willinger (1996). We also find that both quarterly earnings and quarterly cash flow from operations are modeled by the same Brown-Rozeff ARIMA structure, although the autoregressive and seasonal moving-average parameters of the quarterly earnings model are significantly larger than those of the cash-flow prediction model. This finding is consistent with Beaver (1970) and Dechow and Dichev (2002), among others, who argue that accounting accruals induce incremental amounts of serial correlation in the quarterly earnings time series vis-a`-vis the time series of quarterly cash flows. Such findings may be of interest to analysts who wish to derive multi-step-ahead cash-flow predictions, and accounting researchers attempting to adopt a statistical proxy for the market’s expectation of quarterly cash flows. Finally, we propose a forecasting schema by which statistically based cash-flow forecasts are adjusted upwards or downwards via qualitative assessments regarding the economy, industry, and firm by analysts employing fundamental financial analysis.

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Tao, Tianyou, Peng Shi, Hao Wang, Lin Yuan y Sheng Wang. "Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds". Applied Sciences 11, n.º 20 (11 de octubre de 2021): 9441. http://dx.doi.org/10.3390/app11209441.

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Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

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Ribeiro, Matheus Henrique Dal Molin, Viviana Cocco Mariani y Leandro dos Santos Coelho. "Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods". Journal of Biomedical Informatics 111 (noviembre de 2020): 103575. http://dx.doi.org/10.1016/j.jbi.2020.103575.

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Ghobadi, Fatemeh y Doosun Kang. "Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study". Water 14, n.º 22 (14 de noviembre de 2022): 3672. http://dx.doi.org/10.3390/w14223672.

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In recent decades, natural calamities such as drought and flood have caused widespread economic and social damage. Climate change and rapid urbanization contribute to the occurrence of natural disasters. In addition, their destructive impact has been altered, posing significant challenges to the efficiency, equity, and sustainability of water resources allocation and management. Uncertainty estimation in hydrology is essential for water resources management. By quantifying the associated uncertainty of reliable hydrological forecasting, an efficient water resources management plan is obtained. Moreover, reliable forecasting provides significant future information to assist risk assessment. Currently, the majority of hydrological forecasts utilize deterministic approaches. Nevertheless, deterministic forecasting models cannot account for the intrinsic uncertainty of forecasted values. Using the Bayesian deep learning approach, this study developed a probabilistic forecasting model that covers the pertinent subproblem of univariate time series models for multi-step ahead daily streamflow forecasting to quantify epistemic and aleatory uncertainty. The new model implements Bayesian sampling in the Long short-term memory (LSTM) neural network by using variational inference to approximate the posterior distribution. The proposed method is verified with three case studies in the USA and three forecasting horizons. LSTM as a point forecasting neural network model and three probabilistic forecasting models, such as LSTM-BNN, BNN, and LSTM with Monte Carlo (MC) dropout (LSTM-MC), were applied for comparison with the proposed model. The results show that the proposed Bayesian long short-term memory (BLSTM) outperforms the other models in terms of forecasting reliability, sharpness, and overall performance. The results reveal that all probabilistic forecasting models outperformed the deterministic model with a lower RMSE value. Furthermore, the uncertainty estimation results show that BLSTM can handle data with higher variation and peak, particularly for long-term multi-step ahead streamflow forecasting, compared to other models.

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Shaadan, Norshahida y Wan Najiha Wan Mat Din. "Application of Functional Time Series Model in Forecasting Monthly Diurnal API Curves: A Comparison between Multi-Step Ahead and Iterative One-Step Ahead Approach". Malaysian Journal of Fundamental and Applied Sciences 18, n.º 1 (28 de febrero de 2022): 124–37. http://dx.doi.org/10.11113/mjfas.v18n1.2435.

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In Malaysia, Air Pollution Index (API) is used to assess the status of background air quality. The computation of API involved six major air pollutants including PM10, PM2.5, O3, CO, SO2 and NOx. Due to the harmful effect of air pollution, forecasting API is important. This paper introduces the application of Functional Time Series (FTS) model in forecasting monthly diurnal maximum API curves at two selected sites in Peninsular Malaysia; namely Shah Alam Selangor and Pasir Gudang Johor. Two FTS models were compared which include Multi-Step ahead and Iterative One-Step ahead approach. The results show that the Multi-Step ahead model has produced better performance giving the lowest error measures; FMSE, FRMSE and FMAPE compared to Iterative One-Step ahead. This study has shown that FTS model has the advantage because it enables the prediction of continuous API levels within a defined continuum time, which in this study was the interval time within 24 hours. Functional descriptive mean shows a bimodal pattern with a peak at 3.00 pm and the average levels are at a healthy level. Functional mean of API exhibits an increasing pattern after sunrise towards 10.00 am at both sites, which inform that, this is the time with a higher contribution of vehicles emission while the standard deviation differs in the pattern. The model is recommended as an alternative model to be used by the government and environmentalists in providing input for guiding pollution control and protecting public health at the early stage. Furthermore, as for the private sector and industries, this study might provide a predictive analytic tool for forecasting daily API curves instead of a single daily average API value.

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Wang, Dongfeng, Fuqiang Wang y Xiaoyan Wang. "Multi-Step-Ahead Combination Forecasting of Wind Speed Using Artificial Neural Networks". Research Journal of Applied Sciences, Engineering and Technology 5, n.º 23 (28 de mayo de 2013): 5443–49. http://dx.doi.org/10.19026/rjaset.5.4216.

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De Caro, Fabrizio, Jacopo De Stefani, Alfredo Vaccaro y Gianluca Bontempi. "DAFT-E: Feature-Based Multivariate and Multi-Step-Ahead Wind Power Forecasting". IEEE Transactions on Sustainable Energy 13, n.º 2 (abril de 2022): 1199–209. http://dx.doi.org/10.1109/tste.2021.3130949.

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39

Pirbazari, Aida Mehdipour, Ekanki Sharma, Antorweep Chakravorty, Wilfried Elmenreich y Chunming Rong. "An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities". IEEE Access 9 (2021): 36218–40. http://dx.doi.org/10.1109/access.2021.3063066.

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40

Bajirao, Tarate Suryakant, Ahmed Elbeltagi, Manish Kumar y Quoc Bao Pham. "Applicability of machine learning techniques for multi-time step ahead runoff forecasting". Acta Geophysica 70, n.º 2 (8 de marzo de 2022): 757–76. http://dx.doi.org/10.1007/s11600-022-00749-z.

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41

Chang, Li-Chiu, Mohd Amin, Shun-Nien Yang y Fi-John Chang. "Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models". Water 10, n.º 9 (19 de septiembre de 2018): 1283. http://dx.doi.org/10.3390/w10091283.

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A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

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42

Beyaztas, Ufuk y Hanlin Shang. "Machine-Learning-Based Functional Time Series Forecasting: Application to Age-Specific Mortality Rates". Forecasting 4, n.º 1 (18 de marzo de 2022): 394–408. http://dx.doi.org/10.3390/forecast4010022.

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We propose a functional time series method to obtain accurate multi-step-ahead forecasts for age-specific mortality rates. The dynamic functional principal component analysis method is used to decompose the mortality curves into dynamic functional principal components and their associated principal component scores. Machine-learning-based multi-step-ahead forecasting strategies, which automatically learn the underlying structure of the data, are used to obtain the future realization of the principal component scores. The forecasted mortality curves are obtained by combining the dynamic functional principal components and forecasted principal component scores. The point and interval forecast accuracy of the proposed method is evaluated using six age-specific mortality datasets and compared favorably with four existing functional time series methods.

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43

Tishin, Petr M. y Victor S. Buyukli. "The study of the quality of multi-step time series forecasting". Herald of Advanced Information Technology 5, n.º 3 (27 de octubre de 2022): 210–19. http://dx.doi.org/10.15276/hait.05.2022.16.

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The work is devoted to the study of the quality of multistep forecasting of time series using the electricity consumption data for forecasting. Five models of multistep forecasting have been implemented, with their subsequent training and evaluation of the results obtained. The dataset is an upgraded minute-by-minute measurement of four years of electricity consumption. The dataset has been divided into training, validation, and test samples for training and testing models. The implementation is simplified by using the TensorFlow machine learning library, which allows us to conveniently process and present data; build and train neural networks. The TensorFlow functionality also provides standard metrics used to assess the accuracy of time series forecasting, which made it possible to evaluate the obtained models for forecasting the time series of electricity consumption and highlight the best ofthose considered according to the given indicators. The models are built in such a way that they can be used in studies of the quality of time series forecasting in various areas of human life. The problem of multistep forecasting for twenty fourhours ahead, considered in the paper, has not yet been solved for estimating electricity consumption. Theobtainedforecasting accuracy is comparable to recently published methods for estimating electricity consumption used in other conditions.At the same time, the forecasting accuracy of the constructed models has been improved in comparison with other methods.

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44

You, Yujie, Le Zhang, Peng Tao, Suran Liu y Luonan Chen. "Spatiotemporal Transformer Neural Network for Time-Series Forecasting". Entropy 24, n.º 11 (14 de noviembre de 2022): 1651. http://dx.doi.org/10.3390/e24111651.

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Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.

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45

Pei, Shaoqian, Hui Qin, Liqiang Yao, Yongqi Liu, Chao Wang y Jianzhong Zhou. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network". Energies 13, n.º 16 (10 de agosto de 2020): 4121. http://dx.doi.org/10.3390/en13164121.

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Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load.

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46

Gui, Ning, Jieli Lou, Zhifeng Qiu y Weihua Gui. "Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction". Processes 7, n.º 7 (22 de julio de 2019): 473. http://dx.doi.org/10.3390/pr7070473.

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Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two reheater experiments reflect the common physical properties of different reheaters, so the proposed algorithms could be generalized to guide temporal feature selection for other reheaters.

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47

Zhang, J. y K. Nawata. "Multi-step prediction for influenza outbreak by an adjusted long short-term memory". Epidemiology and Infection 146, n.º 7 (2 de abril de 2018): 809–16. http://dx.doi.org/10.1017/s0950268818000705.

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AbstractInfluenza results in approximately 3–5 million annual cases of severe illness and 250 000–500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all <15%, averagely 12.930%. To the best of our knowledge, it is the first time that LSTM has been applied and refined to perform multi-step-ahead prediction for influenza outbreaks. Hopefully, this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.

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48

Shao, Xiaorui y Chang Soo Kim. "Accurate Multi-Site Daily-Ahead Multi-Step PM2.5 Concentrations Forecasting Using Space-Shared CNN-LSTM". Computers, Materials & Continua 70, n.º 3 (2022): 5143–60. http://dx.doi.org/10.32604/cmc.2022.020689.

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49

Tyralis, Hristos y Georgia A. Papacharalampous. "Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow". Advances in Geosciences 45 (17 de agosto de 2018): 147–53. http://dx.doi.org/10.5194/adgeo-45-147-2018.

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Abstract. We assess the performance of the recently introduced Prophet model in multi-step ahead forecasting of monthly streamflow by using a large dataset. Our aim is to compare the results derived through two different approaches. The first approach uses past information about the time series to be forecasted only (standard approach), while the second approach uses exogenous predictor variables alongside with the use of the endogenous ones. The additional information used in the fitting and forecasting processes includes monthly precipitation and/or temperature time series, and their forecasts respectively. Specifically, the exploited exogenous (observed or forecasted) information considered at each time step exclusively concerns the time of interest. The algorithms based on the Prophet model are in total four. Their forecasts are also compared with those obtained using two classical algorithms and two benchmarks. The comparison is performed in terms of four metrics. The findings suggest that the compared approaches are equally useful.

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Barua, S., B. J. C. Perera, A. W. M. Ng y D. Tran. "Drought forecasting using an aggregated drought index and artificial neural network". Journal of Water and Climate Change 1, n.º 3 (1 de septiembre de 2010): 193–206. http://dx.doi.org/10.2166/wcc.2010.000.

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Early forecasting of future drought conditions during continuing dry periods can improve water resources management strategies. In this study, a drought forecasting approach is developed and presented using an aggregated drought index (ADI) and artificial neural network (ANN) using a monthly time step. The use of ADI forecasts the overall availability of water resources beyond the traditional forecasting of rainfall deficiency to represent future drought conditions. The paper compares two types of ANN; namely, recursive multi-step neural networks (RMSNN) and direct multi-step neural networks (DMSNN). The results show that the RMSNN approach is slightly better than the DMSNN approach for forecasts with lead time up to 3 months. The DMSNN approach gives slightly better results than the RMSNN approach when forecast lead time is over 3 months, and can give reasonable results up to 6 months ahead of forecasts.

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