Journal articles: 'One-step forecasting'

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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.

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Ginantra, N. L. W. S. R., Gita Widi Bhawika, GS Achmad Daengs, Pawer Darasa Panjaitan, Mohammad Aryo Arifin, Anjar Wanto, Muhammad Amin, Harly Okprana, Abdullah Syafii, and Umar Anwar. "Performance One-step secant Training Method for Forecasting Cases." Journal of Physics: Conference Series 1933, no. 1 (June 1, 2021): 012032. http://dx.doi.org/10.1088/1742-6596/1933/1/012032.

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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.

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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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HUSAINI, NOOR AIDA, ROZAIDA GHAZALI, NAZRI MOHD NAWI, LOKMAN HAKIM ISMAIL, MUSTAFA MAT DERIS, and TUTUT HERAWAN. "PI-SIGMA NEURAL NETWORK FOR A ONE-STEP-AHEAD TEMPERATURE FORECASTING." International Journal of Computational Intelligence and Applications 13, no. 04 (December 2014): 1450023. http://dx.doi.org/10.1142/s1469026814500230.

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The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-step-ahead temperature forecasting. In this paper, we evaluate the performances of PSNN by comparing the network model with widely used Multilayer Perceptron (MLP). PSNN which is a class of Higher Order Neural Networks (HONN), has a highly regular structure, needs much smaller number of weights and less training time. The PSNN is use to overcome the drawbacks of MLP, which can easily trapped into local minima and prone to overfit. Both network models were trained with standard backpropagation algorithm. Through 1012 experiments, it has been demonstrated that the PSNN has a high practicability and better temperature forecasting for one-step-ahead using historical temperature data of Batu Pahat region.

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Cheng, Ching-Hsue, and Liang-Ying Wei. "One step-ahead ANFIS time series model for forecasting electricity loads." Optimization and Engineering 11, no. 2 (August 26, 2009): 303–17. http://dx.doi.org/10.1007/s11081-009-9091-5.

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Kim, J. R., J. H. Ko, J. H. Im, S. H. Lee, S. H. Kim, C. W. Kim, and T. J. Park. "Forecasting influent flow rate and composition with occasional data for supervisory management system by time series model." Water Science and Technology 53, no. 4-5 (February 1, 2006): 185–92. http://dx.doi.org/10.2166/wst.2006.123.

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The information on the incoming load to wastewater treatment plants is not often available to apply modelling for evaluating the effect of control actions on a full-scale plant. In this paper, a time series model was developed to forecast flow rate, COD, NH+4-N and PO3-4-P in influent by using 250 days data of field plant operation data. The data for 150 days and 100 days were used for model development and model validation, respectively. The missing data were interpolated by the spline method and the time series model. Three different methods were proposed for model development: one model and one-step to seven-step ahead forecasting (Method 1); seven models and one-step-ahead forecasting (Method 2); and one model and one-step-ahead forecasting (Method 3). Method 3 featured only one-step-ahead forecasting that could avoid the accumulated error and give simple estimation of coefficients. Therefore, Method 3 was the reliable approach to developing the time series model for the purpose of this research.

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Mazumder, Satyaki. "Single-step and multiple-step forecasting in one-dimensional single chirp signal using MCMC-based Bayesian analysis." Communications in Statistics - Simulation and Computation 46, no. 4 (December 18, 2016): 2529–47. http://dx.doi.org/10.1080/03610918.2015.1053921.

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Pulido-Calvo, Inmaculada, and Maria Manuela Portela. "Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds." Journal of Hydrology 332, no. 1-2 (January 2007): 1–15. http://dx.doi.org/10.1016/j.jhydrol.2006.06.015.

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Zinkevych, P., S. Baluta, and Iu Kuievda. "Comparative analysis of methods of short-term electric load forecasting one step forward." Scientific Works of National University of Food Technologies 27, no. 3 (June 2021): 62–76. http://dx.doi.org/10.24263/2225-2924-2021-27-3-9.

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Pan, Lu, Sheng Ji Rong, Chang Hui Yu, Chun Xia Jin, and Quan Yin Zhu. "The Influence of Training Step on Price Forecasting Based on Support Vector Machine." Applied Mechanics and Materials 411-414 (September 2013): 2373–76. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2373.

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In order to obtain suit commodity price forecasting model and help consumers have the better reference resources when they buy mobile phones, cell phones price forecasting on training step is discussed in this paper. One year price for ten types mobile phone which extracted from http://www.jd.com/ is used as the original data to improve Support Vector Machine (SVM) model based on the training step. According to this forecasting method, the experiments are implemented under the different training step for different types cell phones depend on the accuracy rata. Comparing the experimental results with the original data, the forecasting average accuracy obtains 94.48 percent. But with the training step growth, the efficiency of model is cutting down unceasingly. Experiment results prove that the research is meaningful and useful and it is not only for consumers, but also for businesses in the cell phones market.

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Raboudi, Naila F., Boujemaa Ait-El-Fquih, Clint Dawson, and Ibrahim Hoteit. "Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter." Monthly Weather Review 147, no. 9 (August 26, 2019): 3283–300. http://dx.doi.org/10.1175/mwr-d-18-0410.1.

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Abstract This work combines two auxiliary techniques, namely the one-step-ahead (OSA) smoothing and the hybrid formulation, to boost the forecasting skills of a storm surge ensemble Kalman filter (EnKF) forecasting system. Bayesian filtering with OSA-smoothing enhances the robustness of the ensemble background statistics by exploiting the data twice: first to constrain the sampling of the forecast ensemble with the future observation, and then to update the resulting ensemble. This is expected to improve the behavior of EnKF-like schemes during the strongly nonlinear surges periods, but requires integrating the ensemble with the forecast model twice, which could be computationally demanding. The hybrid flow-dependent/static formulation of the EnKF background error covariance is then considered to enable the implementation of the filter with a small flow-dependent ensemble size, and thus less model runs. These two methods are combined within an ensemble transform Kalman filter (ETKF). The resulting hybrid ETKF with OSA smoothing is tested, based on twin experiments, using a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico and assimilating pseudo-observations of sea surface levels from a network of buoys. The results of our numerical experiments suggest that the proposed filtering system significantly enhances ADCIRC forecasting skills compared to the standard ETKF without increasing the computational cost.

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KOLOKOLOV, YURY V., and ANNA V. MONOVSKAYA. "MODIFIED BIFURCATION DIAGRAMS IN AN APPROACH TO ONLINE PULSE SYSTEM DYNAMICS FORECASTING." International Journal of Bifurcation and Chaos 16, no. 01 (January 2006): 85–100. http://dx.doi.org/10.1142/s0218127406014605.

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Typically, online forecasting of pulse system dynamics assumes the usage of certain kinds of relevant a priori information. A bifurcation (or a parametric) diagram is one of the most informative forms of presentation of dynamics evolution. However, the data represented in this form is usually insufficient for one-to-one identification of the present system state and dynamics evolution direction, especially when online decision-making is necessary. In this paper two additional modified bifurcation diagrams are introduced. These diagrams provide a framework for deriving an algorithm which can be used to solve, in a step-by-step manner, a complex problem that consists in dynamics monitoring, identification and forecasting.

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Feng, Bin, Jianmin Xu, Yonggang Zhang, and Yongjie Lin. "Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network." Applied Sciences 11, no. 10 (May 13, 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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Hu, Kuang-Hua, Sin-Jin Lin, Ming-Fu Hsu, and Fu-Hsiang Chen. "A dynamic network-based decision architecture for performance evaluation and improvement." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4299–311. http://dx.doi.org/10.3233/jifs-200322.

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This study introduces a dynamic decision architecture that involves three steps for corporate performance forecasting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performance evaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decision making trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the complicated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specific direction, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracing one-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective. By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKOR is subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into random vector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instance learning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases, offers a promising alternative for performance evaluation and forecasting.

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Yan, Ke, Xudong Wang, Yang Du, Ning Jin, Haichao Huang, and Hangxia Zhou. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy." Energies 11, no. 11 (November 8, 2018): 3089. http://dx.doi.org/10.3390/en11113089.

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Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

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Venayagamoorthy, Ganesh Kumar, Kurt Rohrig, and Istvan Erlich. "One Step Ahead: Short-Term Wind Power Forecasting and Intelligent Predictive Control Based on Data Analytics." IEEE Power and Energy Magazine 10, no. 5 (September 2012): 70–78. http://dx.doi.org/10.1109/mpe.2012.2205322.

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Paytyan, Karen. "Development of Forecasting Models Suitable for Metal Trading Companies." Vestnik Volgogradskogo gosudarstvennogo universiteta. Ekonomika, no. 4 (February 2021): 99–109. http://dx.doi.org/10.15688/ek.jvolsu.2020.4.9.

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The volume of world metal consumption is one of the main indicators of the state of economy as a whole. This is explained by the fact that such an industry as construction presents a great demand for these products. At the same time, the volume of construction is growing along with the economy growth, because a healthy market attracts more investment. Therefore, the state of the global metal market is one of the indicators of the state of the global economy as a whole, and the challenges facing this industry are relevant for the entire world market. One of them is forecasting metal prices to make right business decisions. The article presents a practical task that shows the need for forecasting. At the next step, the author developed a criterion for the quality of forecasting, if satisfied, we can talk about the applicability of the model in practice. On a randomly selected time interval, the quality of common statistical forecasting models, such as the pair regression equation and linear models, is analyzed. New models have also been developed that are based both on a technical analysis of exchange quotations of metal prices and on a fundamental one. At the final step, the results of all the models presented in the work were compared with the criterion of applicability developed in practice by the author and the most promising of them were selected.

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Mayur, R., and Baibhav Kumar. "Demand Forecasting of Spare Parts of Automobiles using Gaussian Support Vector Machine." IJOSTHE 6, no. 1 (February 10, 2019): 4. http://dx.doi.org/10.24113/ojssports.v7i1.114.

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Reordering motor vehicle spare parts for the purposes of stock replenishment is an important function of the parts manager in the typical motor dealership. Meaningful reordering requires a reliable forecast of the future demand for items. Production planning and control in remanufacturing are more complex than those in traditional manufacturing. Developing a reliable forecasting process is the first step for optimization of the overall planning process. In remanufacturing, forecasting the timing of demands is one of the critical issues. The current article presents the result of examining the effectiveness of demand forecasting by time series analysis in auto parts remanufacturing. A variety of alternative forecasting techniques were evaluated for this purpose with the aim of selecting one optimal technique to be implemented in an automatic reordering module of a real time computerized inventory management system.

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Ermakov, Anatoly Anatolevich, and Tatyana Klimentyevna Kirillova. "METHOD OF STEPWISE SMOOTHING EXPERIMENTAL DEPENDENCES FOR PROBLEMS OF SHORT-TERM FORECASTING." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 3 (July 30, 2021): 126–33. http://dx.doi.org/10.24143/2072-9502-2021-3-126-133.

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The article considers the correspondence of the step-by-step smoothing method as one of the possible algorithms for short-term forecasting of statistics of equal-current measurements of monotone functions, which represent the values of the determining parameters that evaluate the dynamics of the states of complex technical systems based on the operating time. The true value of the monitored parameter is considered unknown, and the processed measurement values are distributed normally. The measurements are processed by step-by-step smoothing. As a result of processing, a new statistic is formed, which is a forecast statistic, each value of which is a half-sum of the measurement itself and the so-called private forecast. First, the forecasts obtained in this way prove to have the same distribution law as the distribution law of a sample of equally accurate measurements. Second, the forecast trend should be the same as the measurement trend and correspond to the theoretical trend, that is, the true values of the monotone function. Third, the variance of the obtained statistics should not exceed the variance of the original sample. It is inferred that the method of step-by-step smoothing method can be proposed for short-term forecasting

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Geche, F. E., O. Yu Mulesa, A. Ye Batyuk, and V. Yu Smolanka. "LEARNING A COMBINED MODEL OF TIME SERIES FORECASTING." Ukrainian Journal of Information Technology 3, no. 1 (2021): 44–48. http://dx.doi.org/10.23939/ujit2021.03.044.

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The method of construction of the combined model of forecast ing of time series based on basic models of forecasting is developed in the work. The set of basic models is dynamic, ie new prediction models can be included in this set. Models also can be deleted depending on the properties of the time series. For the synthesis of a combined model of forecasting time series with a given forecast step, the optimal step of prehistory is determined at the beginning. Next the functional is constructed. The optimal prehistory step is determined using the autoregression method for a fixed forecast step. It determines the period of time at which the accuracy of models from the base set is analyzed. For each basic model during the process of the construction of the combined model is determined by the weighting factor with which it will be included in the combined model. The weights of the basic models are determined based on their forecasting accuracy for the time period determined by the prehistory step. The weights reflect the degree of influence of the base models on the accuracy of the combined model forecasting. After construction of the combined model, its training is carried out and those basic models which will be included in the final combined model of forecasting are defined. The rule of inclusion of basic models in the combined model is established. While including basic models in the combined forecasting model, their weights are taken into account, which depends on the same parameter. The optimal value of the parameter is determined by minimizing the given functional, which sets the standard deviation between the actual and predicted values of the time series. Weights with optimal parameters are ranked in decreasing order and are used to include basic models in the combined model. As a result of this approach, as predicted values for the real time series show, it was possible to significantly improve the forecasting accuracy of the combined model in many cases. The developed method of training provides the flexibility of the combined model and its application to a wide class of time series. The results obtained in this work contribute to solving the problem of choosing the most effective basic models by synthesizing them into one combined model.

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Sharma, Prateek, and Vipul _. "Forecasting stock index volatility with GARCH models: international evidence." Studies in Economics and Finance 32, no. 4 (October 5, 2015): 445–63. http://dx.doi.org/10.1108/sef-11-2014-0212.

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Purpose – The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices. Design/methodology/approach – Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests. Findings – It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias. Originality/value – This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

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Ribeiro, Matheus, Stéfano Stefenon, José de Lima, Ademir Nied, Viviana Mariani, and Leandro Coelho. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning." Energies 13, no. 19 (October 5, 2020): 5190. http://dx.doi.org/10.3390/en13195190.

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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Kalantari, Mahdi, and Hossein Hassani. "Automatic Grouping in Singular Spectrum Analysis." Forecasting 1, no. 1 (October 30, 2019): 189–204. http://dx.doi.org/10.3390/forecast1010013.

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Singular spectrum analysis (SSA) is a non-parametric forecasting and filtering method that has many applications in a variety of fields such as signal processing, economics and time series analysis. One of the four steps of the SSA, which is called the grouping step, plays a pivotal role in the SSA because reconstruction and forecasting of results are directly affected by the outputs of this step. Usually, the grouping step of SSA is time consuming as the interpretable components are manually selected. An alternative more optimized approach is to apply automatic grouping methods. In this paper, a new dissimilarity measure between two components of a time series that is based on various matrix norms is first proposed. Then, using the new dissimilarity matrices, the capabilities of different hierarchical clustering linkages are compared to identify appropriate groups in the SSA grouping step. The performance of the proposed approach is assessed using the corrected Rand index as validation criterion and utilizing various real-world and simulated time series.

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Huang, Jing, and John Boland. "Performance Analysis for One-Step-Ahead Forecasting of Hybrid Solar and Wind Energy on Short Time Scales." Energies 11, no. 5 (May 2, 2018): 1119. http://dx.doi.org/10.3390/en11051119.

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Duan, Zhi Mei, Yan Jie Zhou, and Xiao Jin Yuan. "Researching of Chaotic Characteristics and Forecasting of Elman Network on the Communication Traffic." Applied Mechanics and Materials 347-350 (August 2013): 3565–70. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3565.

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Communication traffic is a kind of dynamic nonlinear time series affected by various factors; the traditional predication methods cant achieve higher accuracy. In order to improve the communication traffic forecasting accuracy, in this paper, analyzing Chaotic characteristics and Predictability of the Communication traffic based on the Communication traffic data of daily rush hour by collecting, and reconstructing the phase space of the communication traffic time series, proposing a method of building the predication model of the communication traffic by using Elman dynamic neural network, and using proposed model to do one-step forecasting, the experimental results show that this method improves the communication traffic forecasting accuracy. It can provide an effective way for forecasting of the communication traffic.

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Ouyang, Yicun, and Hujun Yin. "Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models." International Journal of Neural Systems 28, no. 04 (March 12, 2018): 1750053. http://dx.doi.org/10.1142/s0129065717500538.

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Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

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Khan, Abdul Jalil, and Parvez Azim. "One-Step-Ahead Forecastability of GARCH (1,1): A Comparative Analysis of USD- and PKR-Based Exchange Rate Volatilities." LAHORE JOURNAL OF ECONOMICS 18, no. 1 (January 1, 2013): 1–38. http://dx.doi.org/10.35536/lje.2013.v18.i1.a1.

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This study aims to capture volatility patterns using GARCH (1,1) models. It evaluates these models to obtain one-step-ahead forecastabilities by employing four major forecasting evaluation criteria, and compares two different currencies— the Pakistan rupee and the US dollar—as domestic and foreign currency-valued exchange rates, respectively. The results show that using an international vehicle currency is favorable in Pakistan’s context. However, the Kuwaiti dinar, Canadian dollar, US dollar, Singapore dollar, Hong Kong dollar, and Malaysian ringgit are found to be preferable when performing direct international transactions. Using the root mean square errors and mean absolute errors techniques, the study also assess the robustness of measuring one-step-ahead forecasts.

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Xiong, Lihua, Kieran M. O’Connor, and Shenglian Guo. "Comparison of three updating schemes using artificial neural network in flow forecasting." Hydrology and Earth System Sciences 8, no. 2 (April 30, 2004): 247–55. http://dx.doi.org/10.5194/hess-8-247-2004.

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Abstract. Three updating schemes using artificial neural network (ANN) in flow forecasting are compared in terms of model efficiency. The first is the ANN model in the simulation mode plus an autoregressive (AR) model. For the ANN model in the simulation model, the input includes the observed rainfall and the previously estimated discharges, while the AR model is used to forecast the flow simulation errors of the ANN model. The second one is the ANN model in the updating mode, i.e. the ANN model uses the observed discharge directly together with the observed rainfall as the input. In this scheme, the weights of the ANN model are obtained by optimisation and then kept fixed in the procedure of flow forecasting. The third one is also the ANN model in the updating mode; however, the weights of the ANN model are no longer fixed but updated at each time step by the backpropagation method using the latest forecast error of the ANN model. These three updating schemes are tested for flow forecasting on ten catchments and it is found that the third updating scheme is more effective than the other two in terms of their efficiency in flow forecasting. Moreover, compared to the first updating scheme, the third scheme is more parsimonious in terms of the number of parameters, since the latter does not need any additional correction model. In conclusion, this paper recommends the ANN model with the backpropagation method, which updates the weights of ANN at each time step according to the latest forecast error, for use in real-time flow forecasting. Keywords: artificial neural network (ANN), updating, flow forecasting, backpropagation method

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Çatık, A. Nazif, and Mehmet Karaçuka. "A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION." Journal of Business Economics and Management 13, no. 2 (April 5, 2012): 275–93. http://dx.doi.org/10.3846/16111699.2011.620135.

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This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.

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Chen, Cathy WS, and K. Khamthong. "Bayesian modelling of nonlinear negative binomial integer-valued GARCHX models." Statistical Modelling 20, no. 6 (July 8, 2019): 537–61. http://dx.doi.org/10.1177/1471082x19845541.

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This study focuses on modelling dengue cases in northeastern Thailand through two meteorological covariates: cumulative rainfall and average maximum temperature. We propose two nonlinear integer-valued GARCHX models (Markov switching and threshold specification) with a negative binomial distribution, as they take into account the stylized features of weekly dengue haemorrhagic fever cases, which contain nonlinear dynamics, lagged dependence, overdispersion, consecutive zeros and asymmetric effects of meteorological covariates. We conduct parameter estimation and one-step-ahead forecasting for two proposed models based on Bayesian Markov chain Monte Carlo (MCMC) methods. A simulation study illustrates that the adaptive MCMC sampling scheme performs well. The empirical results offer strong support for the Markov switching integer-valued GARCHX model over its competitors via Bayes factor and deviance information criterion. We also provide one-step-ahead forecasting based on the prediction interval that offers a useful early warning signal of outbreak detection.

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Unnikrishnan, Poornima, and V. Jothiprakash. "Data-driven multi-time-step ahead daily rainfall forecasting using singular spectrum analysis-based data pre-processing." Journal of Hydroinformatics 20, no. 3 (August 9, 2017): 645–67. http://dx.doi.org/10.2166/hydro.2017.029.

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Abstract Accurate forecasting of rainfall, especially daily time-step rainfall, remains a challenging task for hydrologists' invariance with the existence of several deterministic, stochastic and data-driven models. Several researchers have fine-tuned the hydrological models by using pre-processed input data but improvement rate in prediction of daily time-step rainfall data is not up to the expected level. There are still chances to improve the accuracy of rainfall predictions with an efficient data pre-processing algorithm. Singular spectrum analysis (SSA) is one such technique found to be a very successful data pre-processing algorithm. In the past, the artificial neural network (ANN) model emerged as one of the most successful data-driven techniques in hydrology because of its ability to capture non-linearity and a wide variety of algorithms. This study aims at assessing the advantage of using SSA as a pre-processing algorithm in ANN models. It also compares the performance of a simple ANN model with SSA-ANN model in forecasting single time-step as well as multi-time-step (3-day and 7-day) ahead daily rainfall time series pertaining to Koyna watershed, India. The model performance measures show that data pre-processing using SSA has enhanced the performance of ANN models both in single as well as multi-time-step ahead daily rainfall prediction.

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Vassallo, Daniel, Raghavendra Krishnamurthy, Thomas Sherman, and Harindra J. S. Fernando. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting." Energies 13, no. 20 (October 20, 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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Pavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling." Sensors 20, no. 23 (December 4, 2020): 6931. http://dx.doi.org/10.3390/s20236931.

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Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.

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Huang, Chao-Ming, Sung-Pei Yang, Hong-Tzer Yang, and Yann-Chang Huang. "Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting." Journal of the Chinese Institute of Engineers 37, no. 1 (December 3, 2012): 44–53. http://dx.doi.org/10.1080/02533839.2012.747049.

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Koutroumbas, K., and A. Belehaki. "One-step ahead prediction of <i>fo</i>F2 using time series forecasting techniques." Annales Geophysicae 23, no. 9 (November 22, 2005): 3035–42. http://dx.doi.org/10.5194/angeo-23-3035-2005.

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Abstract. In this paper the problem of one-step ahead prediction of the critical frequency (foF2) of the middle-latitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58000 observations of foF2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Penteli (38° N, 23.5° E), for the period from October 2002 to May 2004. First, the embedding dimension of the dynamical system that generates the above sample is estimated using the false nearest neighbor method. This information is then utilized for the training of the predictors employed in this study, which are the linear predictor, the neural network predictor, the persistence predictor and the k-nearest neighbor predictor. The results obtained by the above predictors suggest that, as far as the mean square error is considered as performance criterion, the first two predictors are significantly better than the latter two predictors. In addition, the results obtained by the linear and the neural network predictors are not significantly different from each other. This may be taken as an indication that a linear model suffices for one step ahead prediction of foF2.

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Kolokas, Nikolaos, Dimosthenis Ioannidis, and Dimitrios Tzovaras. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization." Energies 14, no. 11 (May 28, 2021): 3162. http://dx.doi.org/10.3390/en14113162.

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Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

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Mzyece, Lillian, Mayumbo Nyirenda, Monde K. Kabemba, and Grey Chibawe. "Forecasting Seasonal Rainfall in Zambia – An Artificial Neural Network Approach." Zambia ICT Journal 2, no. 1 (June 29, 2018): 16–24. http://dx.doi.org/10.33260/zictjournal.v2i1.46.

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Weather forecasting is an ever-challenging area of investigation for scientists. It is the application of science and technology in order to predict the state of the atmosphere for a given time and location. Rainfall is one of the weather parameters whose accurate forecasting has significant implications for agriculture and water resource management. In Zambia, agriculture plays a key role in terms of employment and food security. Rainfall forecasting is one of the most complicated and demanding operational responsibilities carried out by meteorological services all over the world. Long-term rainfall prediction is even more a challenging task. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. In this paper, a rainfall forecasting model using Artificial Neural Network is proposed as a model that that can be 'trained' to mimic the knowledge of rainfall forecasting experts. This makes it possible for researchers to adapt different techniques for different stages in the forecasting process. We begin by noting the five main stages in the seasonal rainfall forecasting process. We then apply artificial neural networks at each step. Initial results show that the artificial neural networks can successfully replace the currently used processes together with the expert knowledge. We further propose the use of these neural networks for teaching such forecasting processes, as they make documentation of the forecasting process easier and hence making the educational process of teaching to forecast seasonal rainfall easier as well. Artificial Neural Networks are reliable, handle more data at one time by virtual of being computer based, are less tedious and less dependent on user experience.

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Stepchenko, Arthur, and Jurij Chizhov. "NDVI Short-Term Forecasting Using Recurrent Neural Networks." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 180. http://dx.doi.org/10.17770/etr2015vol3.167.

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In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.

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Lim, Bryan, and Stefan Zohren. "Time-series forecasting with deep learning: a survey." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (February 15, 2021): 20200209. http://dx.doi.org/10.1098/rsta.2020.0209.

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Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting—describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time-series data. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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Yuen, Jonathan. "Bayesian Approaches to Plant Disease Forecasting." Plant Health Progress 4, no. 1 (January 2003): 20. http://dx.doi.org/10.1094/php-2003-1113-06-rv.

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Prediction of disease occurrence is a well known historical theme, and has begun to receive new interest due to internet-based prediction systems. The evaluation of these systems in a quantitative manner is an important step if they are to be used in modern agricultural production. Bayes's theorem is one way in which the performance of such predictors can be studied. In this way, the conditional probability of pest occurrence after a positive or negative prediction can be compared with the unconditional probability of pest occurrence. Both the specificity and the sensitivity of the predictive system are needed, along with the unconditional probability of pest occurrence, in order to make a Bayesian analysis. If there is little information on the prior probability of disease, most predictors will be useful, but for extremely common or extremely rare diseases, a Bayesian analysis indicates that a system predicting disease occurrence or non-occurrence will have limited usefulness. Accepted for publication 29 January 2002. Published 13 November 2003.

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Goumas, Stefanos K., Stavros Kontakos, Aikaterini G. Mathheaki, and Sofoklis Xristoforidis. "Modeling and Forecasting of Tourist Arrivals in Crete Using Statistical Models and Models of Computational Intelligence." International Journal of Operations Research and Information Systems 12, no. 1 (January 2021): 58–72. http://dx.doi.org/10.4018/ijoris.2021010105.

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In the past few decades, tourism has clearly become one of the most prominent economic trends for many countries. For many destinations, this trend will continue to rise, and tourism will become the most dynamic and fastest growing sector of the economy. Thus, the reliable and accurate forecasting of tourism demand is necessary in making decisions for effective and efficient planning of tourism policy. The objective of this paper is the modeling and forecasting the international tourist arrivals to four prefectures of Crete in the year 2012, based on the actual tourist arrivals data over the period 1993 – 2011, using one-step-ahead forecast. In particular, this paper presented a comparative study of time series forecasts of international travel demand for the four prefectures of Crete using a variety of statistical quantitative forecasting models along with neural networks and fuzzy models.

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Stocker, M., and D. J. Noakes. "Evaluating Forecasting Procedures for Predicting Pacific Herring (Clupea harengus pallasi) Recruitment in British Columbia." Canadian Journal of Fisheries and Aquatic Sciences 45, no. 6 (June 1, 1988): 928–35. http://dx.doi.org/10.1139/f88-114.

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The ability of four forecasting methods to generate one-step-ahead forecasts of Pacific herring (Clupea harengus pillasi) recruitment is considered in this paper. Recruitment time series for five coastal stocks and various environmental time series are employed in the analyses. Information up to and including time t is employed to estimate the parameters of each model used to forecast recruitment in year t + 1. Parameter estimates are then updated after each time step with a total of seven one-step-ahead forecasts being generated by each model for each stock. The forecast errors are compared using the five criteria: (1) root mean squared error, (2) mean absolute deviation, (3) mean absolute percent error, (4) median absolute deviation, and (5) median absolute percent error. The results of the study indicate that time series models may provide better forecasts of recruitment for the Strait of Georgia/Johnstone Strait stocks than the other competing procedures. A Ricker stock–recruitment model that takes into account environmental data appears to produce marginally better forecasts for the Central Coast and Queen Charlotte Island stocks, while all models produced equally good/bad forecasts for the Prince Rupert district stocks.

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Bot, Karol, Antonio Ruano, and Maria da Graça Ruano. "Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems." Inventions 6, no. 1 (January 25, 2021): 12. http://dx.doi.org/10.3390/inventions6010012.

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Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.

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Payne, James E., Robert R. Sharp, and Susan A. Simmons. "Forecasting Yearling Prices: A Comparison Of Alternative Time Series Models." Journal of Applied Business Research (JABR) 10, no. 3 (September 22, 2011): 17. http://dx.doi.org/10.19030/jabr.v10i3.5919.

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The thoroughbred breeding industry in North America has fallen on hard times. The health of this industry is often gauged by prices obtained for yearlings at North American auctions, particularly the average prices of summer sales at Keeneland and Saratoga. We examine various exponential smoothing algorithms along with a market-based structural model, as well as an ARIMA model in generating one-step ahead forecasts. The market-based structural model outperforms the other approaches with respect to both in- and out-of-sample forecasting accuracy.

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Thurow, Kerstin, Chao Chen, Steffen Junginger, Norbert Stoll, and Hui Liu. "Transportation robot battery power forecasting based on bidirectional deep-learning method." Transportation Safety and Environment 1, no. 3 (December 12, 2019): 205–11. http://dx.doi.org/10.1093/tse/tdz016.

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Abstract This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning technique. In the proposed model, the on-board battery power data is measured and transmitted. A WPD (wavelet packet decomposition) algorithm is employed to decompose the original collected non-stationary series into several relatively more stable subseries. For each subseries, a deep learning–based predictor – bidirectional long short-term memory (BiLSTM) – is constructed to forecast the battery power voltage from one step to three steps ahead. Two experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model, which shows the highest forecasting accuracy. The obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged, providing effective support for the safe use of transportation robots.

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Vázquez-Prada, M., Á. González, J. B. Gómez, and A. F. Pacheco. "Forecasting characteristic earthquakes in a minimalist model." Nonlinear Processes in Geophysics 10, no. 6 (December 31, 2003): 565–71. http://dx.doi.org/10.5194/npg-10-565-2003.

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Abstract. Using error diagrams, we quantify the forecasting of characteristic-earthquake occurrence in a recently introduced minimalist model. Initially we connect the earthquake alarm at a fixed time after the ocurrence of a characteristic event. The evaluation of this strategy leads to a one-dimensional numerical exploration of the loss function. This first strategy is then refined by considering a classification of the seismic cycles of the model according to the presence, or not, of some factors related to the seismicity observed in the cycle. These factors, statistically speaking, enlarge or shorten the length of the cycles. The independent evaluation of the impact of these factors in the forecast process leads to two-dimensional numerical explorations. Finally, and as a third gradual step in the process of refinement, we combine these factors leading to a three-dimensional exploration. The final improvement in the loss function is about 8.5%.

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CHEN, Jieh-Haur, Chuan Fan ONG, Linzi ZHENG, and Shu-Chien HSU. "FORECASTING SPATIAL DYNAMICS OF THE HOUSING MARKET USING SUPPORT VECTOR MACHINE." International Journal of Strategic Property Management 21, no. 3 (July 11, 2017): 273–83. http://dx.doi.org/10.3846/1648715x.2016.1259190.

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This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.

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Khaki, Mehdi, Boujemaa Ait-El-Fquih, and Ibrahim Hoteit. "Calibrating land hydrological models and enhancing their forecasting skills using an ensemble Kalman filter with one-step-ahead smoothing." Journal of Hydrology 584 (May 2020): 124708. http://dx.doi.org/10.1016/j.jhydrol.2020.124708.

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Duchaud, Jean-Laurent, Cyril Voyant, Alexis Fouilloy, Gilles Notton, and Marie-Laure Nivet. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control." Energies 13, no. 14 (July 10, 2020): 3565. http://dx.doi.org/10.3390/en13143565.

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With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The results are similar for all the models and indicate that the error metric can be reduced up to 0.8% per minute on the time-step for forecasts below one hour and up to 1.7% per ten minutes for forecasts between one and six hours. In addition, it is shown that for short term horizons, it may be advantageous to forecast with a high resolution then average the results at the time-step needed by the energy management system.

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Sun, Sizhou, Lisheng Wei, Jie Xu, and Zhenni Jin. "A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN." Energies 12, no. 3 (January 22, 2019): 334. http://dx.doi.org/10.3390/en12030334.

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Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.

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Journal articles on the topic 'One-step forecasting'

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