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Relevant bibliographies by topics / Rolling Window Time Series Prediction

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Academic literature on the topic 'Rolling Window Time Series Prediction'

Author: Grafiati

Published: 20 February 2023

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Journal articles on the topic "Rolling Window Time Series Prediction"

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Li, Xue Mei, Jia Shu Chen, and Li Xing Ding. "Weighted LS-SVM Method for Building Cooling Load Prediction." Advanced Materials Research 121-122 (June 2010): 606–12. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.606.

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A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks, but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for building cooling load prediction. In order to improve the prediction accuracy of cooling load time series, weighted least squares support vector machine regression (WLS-SVM) method for a chaotic cooling load prediction is proposed. In this method, a sliding time window is built and data in the sliding time

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Wang, Zhihua, Yongbo Zhang, and Huimin Fu. "Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/572173.

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Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR) prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most re

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Shin, Yuna, Taekgeun Kim, Seoksu Hong, et al. "Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods." Water 12, no. 6 (2020): 1822. http://dx.doi.org/10.3390/w12061822.

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Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), and Long–Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1–step ahead recursive prediction to reflect the characteristics of the time series data. In order to increase the prediction

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Wang, Yaping, Chaonan Yang, Di Xu, Jianghua Ge, and Wei Cui. "Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM." Shock and Vibration 2021 (May 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/6615920.

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It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robu

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Gu, Wentao, Yongwei Yang, and Zhenshan Liu. "Forecasting Stock Returns Based on a Time-Varying Factor Weighted Density Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (2018): 831–37. http://dx.doi.org/10.20965/jaciii.2018.p0831.

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Stock returns play an important role in the empirical study of asset pricing, and are often applied in portfolio allocation and performance evaluation. The effect of macroeconomic and financial variables on stock returns is a hot topic and many studies have utilized these variables in time series models to improve the forecasts of stock returns. This study imposes macroeconomic and financial variables as weighting factors on kernel density and establishes a new prediction model – the time-varying factor weighted density model. We apply this model to monthly price data of the Chinese stock inde

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Jeon, Jun-Woo, Okan Duru, Ziaul Haque Munim, and Naima Saeed. "System Dynamics in the Predictive Analytics of Container Freight Rates." Transportation Science 55, no. 4 (2021): 946–67. http://dx.doi.org/10.1287/trsc.2021.1046.

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This study proposes a two-tier cross-validation and backtesting procedure, including expanding and rolling-window test metrics in predictive analytics of container freight rates by utilizing the system dynamics approach. The study utilized system dynamics to represent the nonlinear complex structure of container freight rates for predictive analytics and performed univariate and multivariate time-series analysis as benchmarks of the conventional approach. In particular, the China containerized freight index (CCFI) has been investigated through various parametric methodologies (both conventiona

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González-Enrique, Javier, Juan Jesús Ruiz-Aguilar, José Antonio Moscoso-López, Daniel Urda, Lipika Deka, and Ignacio J. Turias. "Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)." Sensors 21, no. 5 (2021): 1770. http://dx.doi.org/10.3390/s21051770.

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This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants da

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Chen, Cathy W. S., and L. M. Chiu. "Ordinal Time Series Forecasting of the Air Quality Index." Entropy 23, no. 9 (2021): 1167. http://dx.doi.org/10.3390/e23091167.

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This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast classification, and measures the forecast accuracy rates. We employ statistical modeling and machine learning with three weather covariates of daily accumulated precipitation, temperature, and wind direction and also include seasonal dummy variables. The study utilizes four models to forecast air quality levels: (1) an autoregressive model with exogen

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Ge, Xiaohui, Lu Shen, Chaoming Zheng, Peng Li, and Xiaobo Dou. "A Decoupling Rolling Multi-Period Power and Voltage Optimization Strategy in Active Distribution Networks." Energies 13, no. 21 (2020): 5789. http://dx.doi.org/10.3390/en13215789.

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With the increasing penetration of distributed photovoltaics (PVs) in active distribution networks (ADNs), the risk of voltage violations caused by PV uncertainties is significantly exacerbated. Since the conventional voltage regulation strategy is limited by its discrete devices and delay, ADN operators allow PVs to participate in voltage optimization by controlling their power outputs and cooperating with traditional regulation devices. This paper proposes a decoupling rolling multi-period reactive power and voltage optimization strategy considering the strong time coupling between different

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Csernai, Eszter, Gergely Horváth, Michele LeNoue-Newton, et al. "Rolling window-based hepatitis toxicity prediction from routine bloodwork in patients undergoing immune checkpoint inhibitor therapy." Journal of Clinical Oncology 40, no. 16_suppl (2022): e13565-e13565. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e13565.

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e13565 Background: Hepatitis toxicity is one of the most important adverse effects of immune checkpoint inhibitor (ICI) therapy, occurring in approximately 10% of patients. However, when identified early, it can be managed clinically, potentially allowing continuation of ICI treatment. The goal of the study was to evaluate the feasibility and clinical usefulness of an artificial intelligence (AI) model to predict the risk of developing hepatitis toxicity during the course of ICI treatment from routine bloodwork values. Methods: Our model uses a clinical dataset of 2438 patients who received IC

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Conference papers on the topic "Rolling Window Time Series Prediction"

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Li, Lei, Farzad Noorian, Duncan J. M. Moss, and Philip H. W. Leong. "Rolling window time series prediction using MapReduce." In 2014 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2014. http://dx.doi.org/10.1109/iri.2014.7051965.

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Chowdhury, Puja, Philip Conrad, Jason D. Bakos, and Austin Downey. "Time Series Forecasting for Structures Subjected to Nonstationary Inputs." In ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/smasis2021-68338.

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Abstract In this paper, a method for real-time forecasting of the dynamics of structures experiencing nonstationary inputs is described. This is presented as time series predictions across different timescales. The target applications include hypersonic vehicles, space launch systems, real-time prognostics, and monitoring of high-rate and energetic systems. This work presents numerical analysis and experimental results for the real-time implementation of a Fast Fourier Transform (FFT)-based approach for time series forecasting. For this preliminary study, a testbench structure that consists of

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Sheng, Shuangwen, and Yi Guo. "A Prognostics and Health Management Framework for Wind." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91533.

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Abstract Operation and maintenance costs are a major driver for levelized cost of energy of wind power plants and can be reduced through optimized operation and maintenance practices accomplishable by various prognostics and health management (PHM) technologies. In recent years, the wind industry has become more open to adopting PHM solutions, especially those focusing on diagnostics. However, prognostics activities are, in general, still at the research and development stage. On the other hand, the industry has a request to estimate a component’s remaining useful life (RUL) when it has faulte

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Goyal, Vipul, Mengyu Xu, Jayanta Kapat, and Ladislav Vesely. "Prediction Enhancement of Machine Learning Using Time Series Modeling in Gas Turbines." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59082.

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Abstract Blade-path temperature can serve as a precursor of anomalies in combustion system and/or cooling system. Given observations from blade-path temperature sensors of a power plant, we consider prediction of the temperature for each sensor. The only extraneous predictor is the combustion turbine fuel flow, while measurements of other potential predictors are unavailable. Long-memory behavior and heterogeneous variance are observed from the residuals of the generalized additive model. Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Generalized Autoregressive Conditional

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Sukkachart, Piyawadee, Chotiros Surapholchai, and Rajalida Lipikorn. "Time series prediction of retirement mutual fund values using optimal window size selection and support vector regression." In 2017 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2017. http://dx.doi.org/10.1109/icitsi.2017.8267966.

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Taskar, Bhushan, Kie Hian Chua, Tatsuya Akamatsu, et al. "Real-Time Ship Motion Prediction Using Artificial Neural Network." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-80042.

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Abstract Models based on Artificial Neural Networks (ANN) have been developed for predicting ship motions using the information about the wave field around the ship and historical time-series of motions. The ANN models developed in this study were able to predict all six degrees of freedom ship motions in irregular wave conditions with different significant waveheight, peak period and wave directions along with directional spreading. Preparation of training, validation and test datasets has been described along with the development and training of ANNs. The models were tested using the observe

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Bhaumik, Tirtharaj, and Shiladitya Basu. "Applied Data Analytics to Buoy Records for Weather Window Evaluation." In SNAME Maritime Convention. SNAME, 2021. http://dx.doi.org/10.5957/smc-2021-082.

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This paper analyzes weather data recorded by typical oceanographic buoys using data analytics and regression techniques. Time series data over a period of more than four decades (1976 – 2020) are reviewed and profiled. A set of key variables including seasonality, wind speed, wind direction, wave period, wave direction, etc., are screened from the buoy measurements to build a predictive model based on multiple linear regression for significant wave height prediction. A sensitivity analysis is then conducted for the available weather window corresponding to specified threshold operational limit

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Pethel, S. D., C. M. Bowden, and C. C. Sung. "Neural Network Applications to Optical Chaos." In Nonlinear Dynamics in Optical Systems. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nldos.1992.fb4.

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Modeling, characterization and prediction with regard to chaotic systems has been an area of vigorous pursuit for many years. Linear methods, such as Fourier decomposition, do not distinguish chaotic dynamical behavior from noise. Of fundamental importance is the characterization of the physical system or the underlying equations from a given time series or phase space attractor, as well as the influence of noise in coupling across basin boundaries and modifications of the otherwise purely deterministic dynamics. Local approximation methods in relation to arbitrary chaotic attractors, in gener

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Oliveira, Ricardo F., Nelson Rodrigues, José Carlos Teixeira, et al. "A Numerical Study of Solder Paste Rolling Process for PCB Printing." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88035.

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The increasing demand for electronic devices associated with the increasing competitiveness between enterprises, pushes towards process automation to decrease production costs. The reflow soldering has proven to be effective in this regard. This is composed by a series of steps or processes, such as: (a) stencil printing, (b) component placement and (c) reflow oven soldering. Each process has its specific traits that contribute to the overall process efficiency. The present study is directed towards process (a), which includes the rolling of the solder paste over the stencil surface, followed

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Andrade Marin, Antonio, Issa Al Balushi, Adnan Al Ghadani, et al. "Real Time Implementation of ESP Predictive Analytics - Towards Value Realization from Data Science." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207550-ms.

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Abstract Failure Prediction in Oil and Gas Artificial Lift Systems is materializing through the implementation of advanced analytics driven by physics-based models. During the Phase I of this project, two early failure prediction machine learning models were trained offline with historical data and evaluated through a blind test. The next challenge, Phase II, is to operationalize these models on Real-Time and re-assess their accuracy, precision and early prediction (in days) while having the assets focusing on either extending the runtime through optimization, chemical injection, etc. or proac

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Reports on the topic "Rolling Window Time Series Prediction"

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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of ﬁnancial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We ﬁnd that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matr

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