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Academic literature on the topic 'Rolling Window Time Series Prediction'
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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 window are employed to reconstruct the dynamic model. Different weights are assigned to different data in the sliding time window, and the model parameters are refreshed on-line with the rolling of the time window. The results show that the method has more superior performance than other methods like LS-SVM.
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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 recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
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Shin, Yuna, Taekgeun Kim, Seoksu Hong, Seulbi Lee, EunJi Lee, SeungWoo Hong, ChangSik Lee, et al. "Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods." Water 12, no. 6 (June 25, 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 accuracy, the model construction was based on forward variable selection. The fitted models were validated by means of cumulative learning and rolling window learning, as opposed to the hold–out technique. The best results were obtained when the chlorophyll-a concentration was predicted by combining the RNN model with the rolling window learning method. The results suggest that the selection of explanatory variables and 1–step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance.
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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 robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.
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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 (October 20, 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 index and employ the rolling window strategy for out-of-sample forecasting. The result shows that this method improves both statistical and economic measures of out-of-sample forecasting performance.
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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 (July 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 conventional time-series and system dynamics approaches). This study follows a strict validation process consisting of expanding window and rolling-window test procedures for the out-of-sample forecasting accuracy of the proposed systemic model and benchmark models to ensure fair validation. In addition to the predictive features, major governing dynamics are presented in the analysis which may initiate further theoretical discussions on the economics and structure of the container shipping markets. Empirical results indicate that postsample accuracy can be affected by the sample size (training data size) in a given set of methodologies. Considering the economic challenges in the container shipping industry, the proposed methodology may help users to improve their cash-flow visibility and reduce the adverse effects of volatility in container shipping rates.
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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 (March 4, 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 data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.
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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 (September 4, 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 exogenous variables and GARCH (generalized autoregressive conditional heteroskedasticity) errors; (2) an autoregressive multinomial logistic regression; (3) multi-class classification by support vector machine (SVM); (4) neural network autoregression with exogenous variable (NNARX). These models relate to lag-1 AQI values and the previous day’s weather covariates (precipitation and temperature), while wind direction serves as an hour-lag effect based on the idea of nowcasting. The results demonstrate that autoregressive multinomial logistic regression and the SVM method are the best choices for AQI-level predictions regarding the high average and low variation accuracy rates.
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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 (November 5, 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 devices. The mixed-integer voltage optimization model is first decomposed into a long-period master problem for on-load tap changer (OLTC) and multiple short-period subproblems for PV power by Benders decomposition algorithm. Then, based on the high-precision PV and load forecasts, the model predictive control (MPC) method is utilized to modify the independent subproblems into a series of subproblems that roll with the time window, achieving a smooth transition from the current state to the ideal state. The estimated voltage variation in the prediction horizon of MPC is calculated by a simplified discrete equation for OLTC tap and a linearized sensitivity matrix between power and voltage for fast computation. The feasibility of the proposed optimization strategy is demonstrated by performing simulations on a distribution test system.
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Csernai, Eszter, Gergely Horváth, Michele LeNoue-Newton, Kathleen Mittendorf, David Smith, Ben Ho Park, Jan Wolber, and Travis John Osterman. "Rolling window-based hepatitis toxicity prediction from routine bloodwork in patients undergoing immune checkpoint inhibitor therapy." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 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 ICI treatment at the Vanderbilt University Medical Center prior to the end of 2020. Hepatitis toxicity was defined as one or more of ALT, AST, ALKPHOS, BILIRUBIN values exceeding 2.5-times the upper limit of normal value. The available feature set was limited to the routinely available blood test values. All features were normalized to the upper limit of normal and transformed to a discretized symbolic representation, a modified version of Symbolic Aggregate ApproXimation. Motifs were extracted as n-grams from the symbol series, and the counts were used as input features for the predictive model. The study uses standard data science model training and evaluation concepts: train, validation, and test splits were created randomly on the patient level; the reported evaluation metrics are median AUC, TPR, TNR, PPV, NPV over 10 sampling runs. The final, best-performing model architecture is a boosted decision tree model (XGBoost) trained on the last four blood tests to predict hepatitis at the next blood sampling timepoint (i.e., at the time of the next ICI treatment appointment). Results: The best model uses the following eight blood values as features: ALT, AST, ALKPHOS, BILIRUBIN, ALBUMIN, CO2, CALCIUM, and BUN, and achieves an AUC of 0.82 (std. 0.01), with TPR = 0.32 (0.03), TNR = 0.97 (0.005), PPV = 0.18 (0.03), and NPV = 0.99 (0.002). It finds 32% of the timepoints where the patient is going to develop hepatitis toxicity prior to their next treatment, and about 1 in 5 positive predictions are correct. It is important to note that only about 1% of all ‘sequences’ of four consecutive blood tests are followed by hepatitis at the next test. That is, while a relatively large proportion of patients are going to develop hepatitis toxicity during their ICI treatment, the timepoint at which this happens is very uncertain. Conclusions: We demonstrate that an AI model built using only already available patient laboratory data could provide clinically useful input for clinicians to support their ICI treatment decisions to reduce the occurrence of hepatitis toxicity. The dynamic nature and below-patient-level granularity of the model would allow a clinician / clinical trial investigator to make adjustments to the therapy based on individual patient reaction over time.
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 a cantilever beam subjected to nonstationary inputs is used to generate experimental data. First, the data is de-trended, then the time series data is transferred into the frequency domain, and measures for frequency, amplitude, and phase are obtained. Thereafter, select frequency components are collected, transformed back to the time domain, recombined, and then the trend in the data is restored. Finally, the recombined signals are propagated into the future to the selected prediction horizon. This preliminary time series forecasting work is done offline using pre-recorded experimental data, and the FFT-based approach is implemented in a rolling window configuration. Here learning windows of 0.1, 0.5, and 1 s are considered with different computation times simulated. Results demonstrate that the proposed FFT-based approach can maintain a constant prediction horizon at 1 s with sufficient accuracy for the considered system. The performance of the system is quantified using a variety of metrics. Computational speed and prediction accuracy as a function of training time and learning window lengths are examined in this work. The algorithm configuration with the shortest learning window (0.1 s) is shown to converge faster following the nonstationary when compared to algorithm configuration with longer learning windows.
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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 faulted, and this is a key output of prognostics. Systematically presenting PHM technologies to the wind industry by highlighting the RUL prediction need potentially helps speed up its acceptance and provides more benefits from PHM to the industry. In this paper, we introduce a PHM for wind framework. It highlights specifics unique to wind turbines and features integration of data and physics domain information and models. The output of the framework focuses on RUL prediction. To demonstrate its application, a data domain method for wind turbine gearbox fault diagnostics is presented. It uses supervisory control and data acquisition system time series data, normalizes gearbox temperature measurements with reference to environmental temperature and turbine power, and leverages big data analytics and machine-learning techniques to make the model scalable and the diagnostics process automatic. Another physics-domain modeling method for RUL prediction of wind turbine gearbox high-speed-stage bearings failed by axial cracks is also discussed. Bearing axial cracking has been shown to be the prevalent wind turbine gearbox failure mode experienced in the field and is different from rolling contact fatigue, which is targeted during the bearing design stage. The method uses probability of failure as a component reliability assessment and RUL prediction metric, which can be expanded to other drivetrain components or failure modes. The presented PHM for wind framework is generic and applicable to both land-based and offshore wind turbines.
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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 Heteroskedasticity (GARCH) models are employed to fit the residual process, which significantly improve the prediction. Rolling one-step-ahead forecast is studied for each of the sixteen univariate blade-path temperature sensors. Their conditional variances are also estimated. Numerical experiments are performed with manually generated perturbation to evaluate the specificity and sensitivity of the prediction. Abrupt changes in the temperature are considered in the numerical study with various jump sizes. We also consider slowly increasing trend in the blade-path temperature with different slopes. Our prediction is sensitive given reasonable signal-to-noise ratio. It also has a much lower false positive rate than the generalized additive model prediction from the combustion turbine fuel flow. Difference between the real-time forecast and observation can be deployed to test for anomalies.
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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, Ryo Kakuta, Song Wen Yeow, Ryosuke Niki, Keita Nishizawa, and Allan Magee. "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 observed wave conditions recorded by a wave radar installed onboard the ship. A physics-based approach has been applied when selecting the length of input and output data. The effect of input and output window length on the accuracy of results was further studied by developing two sets of ANNs with different length of input and output window. Performance of both sets of ANNs on training, validation and test datasets has been presented along with detailed investigation on test dataset. Reducing the length of input window and increasing the length of output window was seen to reduce the accuracy of prediction.
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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 limits of the significant wave height. Key insights are presented along with suggestions for future work to assist marine operators in planning and derisking offshore operations. Utilizing the algorithms and workflows presented in this paper, a user can increase confidence in weather window prediction, and develop safer, efficient offshore operation plans.
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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. Washington, D.C.: 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 general, are insufficient to deduce the generating equations and conditions.1 A forward-feed, hidden-layer, neural network (FFNN), on the other hand, is manifestly a function generator, and when trained adequately on a chaotic time series, is shown to constitute a global approximation to the attractor.2 That is, a FFNN, trained upon a chaotic time series, becomes a functional realization of that time series, in the global sense. Furthermore, a FFNN is shown to course grain the noise, in a time series during training, in the least-squares sense.3 The functional realization property of the FFNN allows the possibility for data window extension, once it is trained on a stationary time series, which can be, in fact, a rather narrow window. This is accomplished in the FFNN by choosing the input, in the form of delay coordinates, from a portion of the original time series which was not part of the training set, and then feeding the output into the input; thus, the trained FFNN becomes self-generating, and facilitates data window extension.
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Oliveira, Ricardo F., Nelson Rodrigues, José Carlos Teixeira, Duarte Santos, Delfim Soares, Maria F. Cerqueira, and Senhorinha F. C. F. Teixeira. "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 by the subsequent filling of the stencil apertures. Several parameters influence the solder paste behaviour and thus the effectiveness of the rolling process. This work focuses on the solder paste non-Newtonian viscosity properties, with the solder paste presenting a thixotropic behaviour, necessary for the filling of the stencil apertures. Although the increase in the squeegee velocity causes extra shear in the solder paste and consequently lower viscosity, the excess of velocity may cause defects in the aperture filling process. In addition, during the rolling process, air may become entrapped in the solder paste. The complexity of this process is addressed by numerical simulation, in particular, using the work-package ANSYS to study the solder paste progress, during the rolling process, as well as the parameters influencing it. The fluid flow simulation is solved using the solver FLUENT®, a simplified 2D domain with real case dimensions, a transient prediction of the viscosity, which is a function of the solder paste solicitation, and finally by using the Volume of Fluid (VOF) method to track the solder-air interface boundary. Dynamic meshing methods are also employed to replicate the movement of the squeegee wall, in its task to push the solder paste tumble over the stencil. This study enlightens the role played by the printing velocity in the stencil aperture filling, a logarithm correlation can be found between them. It was found that lower print velocities provide better results than higher speeds. It was observed that the back tip of the squeegee blade causes a partial removal of the solder paste from the aperture, which is higher for faster print processes. An analysis of the filling process over time concluded that, independently of the printing velocity, 90% of the filling occurs in the first quarter of the process.
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Andrade Marin, Antonio, Issa Al Balushi, Adnan Al Ghadani, Hassana Al Abri, Abdullah Khalfan Said Al Zaabi, Khalid Dhuhli, Issa Al Hadhrami, 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 proactive pump replacement (PPR) for high producers wells with triggered early prediction alarms. The paper details Phase II of live prediction for two assets consisting of 740 wells to enable data-driven insights in engineers’ daily workflow. In Phase I, a collaboration between SMEs and Data Scientists was established to build two failure prediction models for Electrical Submersible Pumps (ESP) using historical data that could identify failure prone wells along with the component at risk with high precision. Phase II entails the development of a Real-Time scoring pipeline to avail daily insights from this model for live wells. To achieve this, PDO leveraged its Digital Infrastructure for extraction of high-resolution measured data for 750 wells daily. A Well Management System (WMS) automatically sustains physics-based ESP models to calculate engineering variables from nodal analysis. Measured and engineered data are sampled, and referencing learnt patterns, the machine learning algorithm (MLA) estimates the probability of failure based on a daily rolling data window. An Exception Based Surveillance (EBS) system tracks well failure probability and highlights affected wells based on business logic. A visualization is developed to facilitate EBS interpretation. All the above steps are automated and synchronized among data historian, WMS and EBS System to operate on a daily schedule. From the Asset, at each highlighted exception, a focus team of well owners and SME initiate a review to correlate the failure probability with ESP signatures to validate the alarm. Aided by physics-based well models, action is directed either towards a) optimization, b) troubleshooting or c) proactive pump replacement in case of inevitable failure conditions. This workflow enables IT infrastructure and Asset readiness to benefit from various modeling initiatives in subsequent phases. Live Implementation of Exceptions from Predictive Analytics is an effective complement to well owners for prioritization of well reviews. Based on alarm validity, risk of failure and underperformance – optimizations, PPRs or workover scheduling are performed with reliability. This methodology would enable a Phase III of scaling up in Real-Time with growing assets wherethe system would be periodically retrained on True Negatives and maintained automatically with minimum manual intervention. It is experienced that a high precision model alone is not enough to reap the benefits of Predictive Analytics. The ability to operate in a production mode and embedding insights into decisions and actions, determines ROI on Data Science initiatives. Digital Infrastructure, a Real Time Well Modeling Platform and Cognitive adaptation of analytics by Well Owners are key for this operationalization that demands reliable data quality, computational efficiency, and data-driven decisions philosophy.
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.
Full textAbstract:
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 matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.