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Relevant bibliographies by topics / One-step forecasting

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Academic literature on the topic 'One-step forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "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, et al. "Performance One-step secant Training Method for Forecasting Cases." Journal of Physics: Conference Series 1933, no. 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 (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

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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 (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

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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 (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, et al. "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 (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

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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 (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 (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 (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 result

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Dissertations / Theses on the topic "One-step forecasting"

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Vasudevan, Sneha. "One-Step-Ahead Load Forecasting for Smart Grid Applications." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1323312608.

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Li, Yang. "The time-series approaches in forecasting one-step-ahead cash-flow data of mining companies listed on the Johannesburg Stock Exchange." Thesis, University of the Western Cape, 2007. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_1552_1254470577.

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<p>Previous research pertaining to the financial aspect of the mining industry has focused predominantly on mining products' values and the companies' sensitivity to exchange rates. There has been very little empirical research carries out in the field of the statistical behaviour of mning companies' cash flow data. This paper aimed to study the time-series behaviour of the cash flow data series of JSE listed mining companies.</p>

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Raboudi, Naila Mohammed Fathi. "A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting." Thesis, 2016. http://hdl.handle.net/10754/621969.

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The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-sc

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Books on the topic "One-step forecasting"

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Ramaswamy, Srichander. One-step prediction of financial time series. Bank for International Settlements, Monetary and Economic Dept., 1998.

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Devil's tango: How I learned the Fukushima one step at a time. Wings Press, 2012.

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Book chapters on the topic "One-step forecasting"

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Yue, Xiaoyun, Yajun Guo, Jinran Wang, Xuezhi Mao, and Xiaoqing Lei. "Water Pollution Forecasting Model of the Back-Propagation Neural Network Based on One Step Secant Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16336-4_61.

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Nayak, Sarat Chandra, Bijan Bihari Misra, and Himansu Sekhar Behera. "On Developing and Performance Evaluation of Adaptive Second Order Neural Network With GA-Based Training (ASONN-GA) for Financial Time Series Prediction." In Advancements in Applied Metaheuristic Computing. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4151-6.ch010.

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Financial time series forecasting has been regarded as a challenging issue because of successful prediction could yield significant profit, hence require an efficient prediction system. Conventional ANN based models are not competent systems. Higher order neural networks have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence. With the aim of achieving improved forecasting accuracy, this article develops and evaluates the performance of an adaptive single layer second order neural network with GA based training (ASONN-GA). The global search ability of GA has been incorporated with the better generalization ability of a second order neural network and the model is found quite capable in handling the uncertainties and nonlinearities associated with the financial time series. The model takes minimal input data and considered the partially optimized weight set from previous training, hence a significant reduction in training time. The efficiency of the model has been evaluated by forecasting one-step-ahead closing prices and exchange rates of five real stock markets and it is revealed that the ASONN-GA model achieves better forecasting accuracy over other state of the art models.

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Christodoulos, Charisios, Christos Michalakelis, and Thomas Sphicopoulos. "On the Efficiency of Grey Modeling in Early-Stage Technological Diffusion Forecasting." In Technology Adoption and Social Issues. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5201-7.ch036.

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The issue of how to obtain an accurate short-term forecast in the beginning stage of the technological diffusion is of great importance for policy makers, researchers and managers. Time-series forecasting has been noticeably neglected in the specific research area due to the prerequisite of having enough data in order to create a time-series. In this paper, Grey modeling is examined as an alternative tool for technology diffusion forecasting in the early diffusion process, where the commonly used aggregate diffusion models usually fail to deliver accurate forecasts. Grey modeling is a unique time-series methodology that requires only a few data points in order to make a forecast. The GM(1,1) model is tested against a classic aggregate diffusion model, the Gompertz model, using only the first four data of mobile broadband diffusion to make an one-step-ahead prediction. The results in the EU15 countries reveal that the Grey model outperforms the Gompertz model in every case, thus stimulating new research guidelines in terms of combinations of the two approaches and further investigation of the value of Grey modeling in the specific area.

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Conference papers on the topic "One-step forecasting"

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Bernardo-Torres, Abraham, and Pilar Gomez-Gil. "One-step forecasting of seismograms using multi-layer perceptrons." In 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2009). IEEE, 2009. http://dx.doi.org/10.1109/iceee.2009.5393349.

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Pindoriya, N. M., S. N. Singh, and S. K. Singh. "One-step-ahead hourly Load Forecasting using artificial Neural Network." In 2009 International Conference on Power Systems. IEEE, 2009. http://dx.doi.org/10.1109/icpws.2009.5442744.

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Peksova-Szolgayova, Elena. "HYBRID MODEL FOR ONE STEP AHEAD FORECASTING OF DAILY RIVER FLOWS." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017h/33/s12.024.

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Shynkevich, Yauheniya, T. M. McGinnity, Sonya Coleman, Yuhua Li, and Ammar Belatreche. "Forecasting stock price directional movements using technical indicators: Investigating window size effects on one-step-ahead forecasting." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2014. http://dx.doi.org/10.1109/cifer.2014.6924093.

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Karunaratne, Pasan, Masud Moshtaghi, Shanika Karunasekera, Aaron Harwood, and Trevor Cohn. "Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/277.

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In time-series forecasting, regression is a popular method, with Gaussian Process Regression widely held to be the state of the art. The versatility of Gaussian Processes has led to them being used in many varied application domains. However, though many real-world applications involve data which follows a working-week structure, where weekends exhibit substantially different behavior to weekdays, methods for explicit modelling of working-week effects in Gaussian Process Regression models have not been proposed. Not explicitly modelling the working week fails to incorporate a signiﬁcant source

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Mészáros, Jakub, Pavol Miklánek, and Pavla Pekárová. "ESTIMATION OF THE T-YEAR SPECIFIC DISCHARGE USING THE REGIONALISED SKEWNESS COEFFICIENT OF THE LOG-PEARSON TYPE III DISTRIBUTION." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.09.

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In this paper the results are presented of estimation of T-year specific discharge of several streams in two regions in Slovakia. The Qmax time series used in the study were observed at water gauges from lowland Slovak part of the Morava River basin, and from the mountainous Belá River basin. For estimating the design values, we have studied the use of only one type of probability distribution, namely the Log-Pearson Type III Distribution (LP3 distribution). The use of only one type of distribution brings several benefits, e.g. possibility of the regionalization of the distribution parameters

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Muehlfeld, Christian M., and Sudhakar M. Pandit. "Speed and Throttle Position Forecasting on a Parallel Hybrid Electric Vehicle." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42262.

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Included in this paper is the forecasting of the speed and throttle position on a thru-the-road parallel hybrid electric vehicle (HEV). This thru-the-road parallel hybrid design is implemented in a 2002 model year Ford Explorer XLT, which is also the Michigan Tech Future Truck. Data Dependent Systems (DDS) forecasting is used in a feedforward control algorithm to improve the fuel economy and to improve the drivability. It provides a one step ahead forecast, thereby allowing the control algorithm to always be a step ahead, utilizing the engine and electric motor in their most efficient ranges.

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Janál, Petr, and Tomáš Kozel. "FUZZY LOGIC BASED FLASH FLOOD FORECAST." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.10.

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The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurren

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Iurasov, Aleksei, and Giedre Stanelyte. "Study of different data science methods for demand prediction and replenishment forecasting at retail network." In 11th International Scientific Conference „Business and Management 2020“. VGTU Technika, 2020. http://dx.doi.org/10.3846/bm.2020.604.

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The demand prediction becoming an essential tool to remain or even lead in the competitionamong the retail businesses. A well-done demand prediction model could help retailer to track the level ofinventory, orders and sales in the most effective way in which the best results could be achieved. However,there are many different methods and opinions of how to create a demand prediction model. In this paper,we will analyse the most commonly used methods of Linear regression, Logistic Regression, ProbabilisticNeural Network, Bayesian Additive Regression Trees, Random Forest and Fuzzy Logic with the

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Tayeb, Yousef, Faisal Naseef, and Amell Ghamdi. "Automatic Simulation Data Update: A New Innovative Way to Expedite Historical Data Extension for Models." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21187-ms.

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Abstract Historical data in reservoir simulation models becomes outdated with time and the gap between the latest history matched model and the current situation increases. Time is essential, especially with escalated numbers of simulation models. Therefore, updating models in a frequent manner requires a new approach to maximize human resources and deliver timely answers. Engineers must also ensure that the existing quality checked input is secured and that data integrity is maintained during updates. In the conventional method of updating simulation model data, engineers go through several s

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