Готові списки джерел за темами / Multi-step ahead forecasting

Добірка наукової літератури з теми "Multi-step ahead forecasting"

Автор: Grafiati

Опубліковано: 14 березня 2023

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Зміст

Статті в журналах
Дисертації
Частини книг
Тези доповідей конференцій
Звіти організацій

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Multi-step ahead forecasting".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Статті в журналах з теми "Multi-step ahead forecasting"

Kaboudan, M. A. "WAVELETS IN MULTI-STEP-AHEAD FORECASTING." IFAC Proceedings Volumes 38, no. 1 (2005): 36–41. http://dx.doi.org/10.3182/20050703-6-cz-1902.02242.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Findley, D. F. "Model Selection for Multi-Step-Ahead Forecasting." IFAC Proceedings Volumes 18, no. 5 (July 1985): 1039–44. http://dx.doi.org/10.1016/s1474-6670(17)60699-2.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Hayder, Gasim, Mahmud Iwan Solihin, and M. R. N. Najwa. "Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM." H2Open Journal 5, no. 1 (January 25, 2022): 43–60. http://dx.doi.org/10.2166/h2oj.2022.134.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

CHANG, FI-JOHN, YEN-MING CHIANG, and LI-CHIU CHANG. "Multi-step-ahead neural networks for flood forecasting." Hydrological Sciences Journal 52, no. 1 (February 2007): 114–30. http://dx.doi.org/10.1623/hysj.52.1.114.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

McElroy, Tucker S., and David F. Findley. "Selection between models through multi-step-ahead forecasting." Journal of Statistical Planning and Inference 140, no. 12 (December 2010): 3655–75. http://dx.doi.org/10.1016/j.jspi.2010.04.032.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Xiong, Tao, Yukun Bao, and Zhongyi Hu. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices." Energy Economics 40 (November 2013): 405–15. http://dx.doi.org/10.1016/j.eneco.2013.07.028.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Xiong, Shenghua, Chunfeng Wang, Zhenming Fang, and 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, no. 1 (January 2, 2019): 147. http://dx.doi.org/10.3390/en12010147.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Li, Fang, Lihua Zhang, Xiao Wang, and Shihu Liu. "Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2023–39. http://dx.doi.org/10.3233/jifs-211405.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Suradhaniwar, Saurabh, Soumyashree Kar, Surya S. Durbha, and Adinarayana Jagarlapudi. "Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies." Sensors 21, no. 7 (April 1, 2021): 2430. http://dx.doi.org/10.3390/s21072430.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Su, Haokun, Xiangang Peng, Hanyu Liu, Huan Quan, Kaitong Wu, and Zhiwen Chen. "Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network." Mathematics 10, no. 14 (July 6, 2022): 2366. http://dx.doi.org/10.3390/math10142366.

Повний текст джерела

Анотація:

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.

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Дисертації з теми "Multi-step ahead forecasting"

Leon, Ojeda Luis. "Short-term multi-step ahead traffic forecasting." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT081/document.

Повний текст джерела

Анотація:

Dans le cadre des systèmes de transport intelligents (ITS), cette thèse concerne la conception d'une méthodologie de prédiction, en temps réel et pour différents horizons, du temps de parcours à partir des données de vitesse et de débit d'une route instrumentée. Pour atteindre cet objectif, deux approches sont considérées dans cette thèse. La première approche, dite « sans modèle », utilise exclusivement des mesures de vitesse. Grâce à l'utilisation astucieuse des données historiques, nous avons résolu le problème de prédiction comme étant un problème de filtrage. Pour ce faire, des données historiques sont utilisées pour construire des pseudo-observations qui alimentent un filtre de Kalman adaptatif (AKF). Sous une hypothèse de Gaussianité, les statistiques du bruit de processus sont estimées en temps-réel, tandis que les statistiques du pseudo-bruit d'observation sont déduites des données historiques adéquatement classées. La seconde approche, dite ‘'basée-modèle'', utilise principalement des mesures de débit et de vitesse. Contrairement à la précédente approche où la résolution spatiale est fixée par l'emplacement des capteurs, une discrétisation spatiale plus fine est considérée. Celle-ci s'avère possible grâce à l'utilisation du modèle CTM (Cell Transmission Model). Un observateur d'état commuté, de type Luenberger, permet d'estimer les états internes (densités des cellules). En utilisant uniquement les prédictions des débits des conditions frontières via une approche de type AKF similaire à celle développée dans la première approche, le modèle CTM contraint permet de prédire les densités des cellules et d'en déduire les vitesses et le temps de parcours. Les méthodes développées ont été validées expérimentalement en considérant la rocade sud grenobloise comme cas d'étude. Les résultats montrent que les deux méthodes présentent de bonnes performances de prédiction. Les méthodes proposées performent mieux que celles basées sur une utilisation directe des moyennes historiques. Pour l'ensemble des données considérées, l'étude a également montré que l'approche ‘'basée modèle‘' est plus adaptée pour des horizons de prédictions de moins de 30 min
This 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

Стилі APA, Harvard, Vancouver, ISO та ін.

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.

Повний текст джерела

Анотація:

How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series.

Historically, 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

Стилі APA, Harvard, Vancouver, ISO та ін.

Pathirana, Vindya Kumari. "Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5757.

Повний текст джерела

Анотація:

Foreign exchange (FX) rate forecasting has been a challenging area of study in the past. Various linear and nonlinear methods have been used to forecast FX rates. As the currency data are nonlinear and highly correlated, forecasting through nonlinear dynamical systems is becoming more relevant. The nearest neighbor (NN) algorithm is one of the most commonly used nonlinear pattern recognition and forecasting methods that outperforms the available linear forecasting methods for the high frequency foreign exchange data. The basic idea behind the NN is to capture the local behavior of the data by selecting the instances having similar dynamic behavior. The most relevant k number of histories to the present dynamical structure are the only past values used to predict the future. Due to this reason, NN algorithm is also known as the k-nearest neighbor algorithm (k-NN). Here k represents the number of chosen neighbors. In the k-nearest neighbor forecasting procedure, similar instances are captured through a distance function. Since the forecasts completely depend on the chosen nearest neighbors, the distance plays a key role in the k-NN algorithm. By choosing an appropriate distance, we can improve the performance of the algorithm significantly. The most commonly used distance for k-NN forecasting in the past was the Euclidean distance. Due to possible correlation among vectors at different time frames, distances based on deterministic vectors, such as Euclidean, are not very appropriate when applying for foreign exchange data. Since Mahalanobis distance captures the correlations, we suggest using this distance in the selection of neighbors. In the present study, we used five different foreign currencies, which are among the most traded currencies, to compare the performances of the k-NN algorithm with traditional Euclidean and Absolute distances to performances with the proposed Mahalanobis distance. The performances were compared in two ways: (i) forecast accuracy and (ii) transforming their forecasts in to a more effective technical trading rule. The results were obtained with real FX trading data, and the results showed that the method introduced in this work outperforms the other popular methods. Furthermore, we conducted a thorough investigation of optimal parameter choice with different distance measures. We adopted the concept of distance based weighting to the NN and compared the performances with traditional unweighted NN algorithm based forecasting. Time series forecasting methods, such as Auto regressive integrated moving average process (ARIMA), are widely used in many ares of time series as a forecasting technique. We compared the performances of proposed Mahalanobis distance based k-NN forecasting procedure with the traditional general ARIM- based forecasting algorithm. In this case the forecasts were also transformed into a technical trading strategy to create buy and sell signals. The two methods were evaluated for their forecasting accuracy and trading performances. Multi-step ahead forecasting is an important aspect of time series forecasting. Even though many researchers claim that the k-Nearest Neighbor forecasting procedure outperforms the linear forecasting methods for financial time series data, and the available work in the literature supports this claim with one step ahead forecasting. One of our goals in this work was to improve FX trading with multi-step ahead forecasting. A popular multi-step ahead forecasting strategy was adopted in our work to obtain more than one day ahead forecasts. We performed a comparative study on the performance of single step ahead trading strategy and multi-step ahead trading strategy by using five foreign currency data with Mahalanobis distance based k-nearest neighbor algorithm.

Стилі APA, Harvard, Vancouver, ISO та ін.

Частини книг з теми "Multi-step ahead forecasting"

Barba Maggi, Lida Mercedes. "Multi-Step Ahead Forecasting." In Multiscale Forecasting Models, 49–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94992-5_3.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Dzalbs, Ivars, and Tatiana Kalganova. "Multi-step Ahead Forecasting Using Cartesian Genetic Programming." In Inspired by Nature, 235–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67997-6_11.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Hajizadeh, Ehsan, and Amin Hajizadeh. "Multi-step Ahead Power Demand Forecasting in Smart Grid." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Chang, F. J., Y. C. Lo, P. A. Chen, L. C. Chang, and M. C. Shieh. "Multi-Step-Ahead Reservoir Inflow Forecasting by Artificial Intelligence Techniques." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Nourani, Vahid, Parnian Ghaneei, and Elnaz Sharghi. "Multi-Step-Ahead Forecasting of Groundwater Level Using Model Ensemble Technique." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Shao, Haijian, Chunlong Hu, Xing Deng, and Dengbiao Jiang. "Multi-step Ahead Time Series Forecasting Based on the Improved Process Neural Networks." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Koprinska, Irena, Mashud Rana, and Ashfaqur Rahman. "Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

De Stefani, Jacopo, Olivier Caelen, Dalila Hattab, Yann-Aël Le Borgne, and Gianluca Bontempi. "A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting." In ECML PKDD 2018 Workshops, 7–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13463-1_1.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Thu, Nguyen Thi Hoai, Pham Nang Van, and Phan Quoc Bao. "Multi-step Ahead Wind Speed Forecasting Based on a Bi-LSTM Network Combined with Decomposition Technique." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Dabrowski, Joel Janek, YiFan Zhang, and Ashfaqur Rahman. "ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-step-Ahead Time-Series Forecasting." In Neural Information Processing, 579–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_48.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Multi-step ahead forecasting"

Garcia-Vega, Sergio, German Castellanos-Dominguez, Michel Verleysen, and John A. Lee. "Multi-step-ahead forecasting using kernel adaptive filtering." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727463.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Gupta, Priya, and Rhythm Singh. "Univariate model for hour ahead multi-step solar irradiance forecasting." In 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). IEEE, 2021. http://dx.doi.org/10.1109/pvsc43889.2021.9519002.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Peralta Donate, Juan, Paulo Cortez, Araceli Sanchis de Miguel, and German Gutierrez Sanchez. "Evolving sparsely connected neural networks for multi-step ahead forecasting." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2001982.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Hong, Chao-Fu, Yung-Sheng Liao, Mu-Hua Lin, and Tsai-Hsia Hong. "A Study of Improving the Coherence in Multi-Step Ahead Forecasting." In 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.152.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Cao, Lili, Huajing Fang, and Xiaoyong Liu. "Multi-step ahead forecasting for fault prognosis using Hidden Markov Model." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162191.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

da Silva, Ramon Gomes, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, Viviana Mariani, and Leandro Coelho. "WIND ENERGY MULTI-STEP AHEAD FORECASTING BASED ON VARIATIONAL MODE DECOMPOSITION." In 18th Brazilian Congress of Thermal Sciences and Engineering. ABCM, 2020. http://dx.doi.org/10.26678/abcm.encit2020.cit20-0400.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Fu, Yiwei, Wei Hu, Maolin Tang, Rui Yu, and Baisi Liu. "Multi-step Ahead Wind Power Forecasting Based on Recurrent Neural Networks." In 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2018. http://dx.doi.org/10.1109/appeec.2018.8566471.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Bonetto, Riccardo, and Michele Rossi. "Parallel multi-step ahead power demand forecasting through NAR neural networks." In 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2016. http://dx.doi.org/10.1109/smartgridcomm.2016.7778780.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Kaur, Devinder, Shama Naz Islam, and Md Apel Mahmud. "A Bayesian Probabilistic Technique for Multi-Step Ahead Renewable Generation Forecasting." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Duong, Hieu N., Vu H. Nguyen, Tam M. Nguyen, Hien T. Nguyen, and Vaclav Snasel. "The Hybrid Approaches for Forecasting Real Time Multi-step-ahead Boiler Efficiency." In 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Звіти організацій з теми "Multi-step ahead forecasting"

McCracken, Michael W., and 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.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!