scholarly journals Enhancing Satellite Clock Bias Prediction Accuracy in the Case of Jumps with an Improved Grey Model

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ye Yu ◽  
Mo Huang ◽  
Tao Duan ◽  
Changyuan Wang ◽  
Rui Hu
Keyword(s):  
Prediction Accuracy ◽  
Least Squares Method ◽  
Moving Average ◽  
Arma Model ◽  
Valuable Insight ◽  
Least Absolute Deviations ◽  
Maximum Improvement ◽  
Improved Model ◽  
Prediction Approach

High accuracy and reliable predictions of the bias of in-orbit atomic clocks are crucial to the application of satellites, while their clocks cannot transfer time information with the earth stations. It brings forward a new short-term, mid-long-term, and long-term prediction approach with the grey predicting model (GM(1, 1)) improved by the least absolute deviations (GM(1, 1)-LAD) when there are abnormal cases (larger fluctuations, jumps, and/or singular points) in SCBs. Firstly, it introduces the basic GM(1, 1) models. As the parameters of the conventional GM(1, 1) model determined by the least squares method (LSM) is not the best in these cases, leading to magnify the fitting errors at the abnormal points, the least absolute deviations (LAD) is used to optimize the conventional GM(1, 1) model. Since the objective function is a nondifferentiable characteristic, some function transformation is inducted. Then, the linear programming and the simplex method are used to solve it. Moreover, to validate the prediction performances of the improved model, six prediction experiments are performed. Compared with those of the conventional GM(1, 1) model and autoregressive moving average (ARMA) model, results indicate that (1) the improved model is more adaptable to SCBs predictions of the abnormal cases; (2) the root mean square (RMS) improvement for the improved model are 5.7%∼81.7% and 6.6%∼88.3%, respectively; (3) the maximum improvement of the pseudorange errors (PE) and mean absolute errors (MAE) for the improved model could reach up to 88.30%, 89.70%, and 87.20%, 85.30%, respectively. These results suggest that our improved method can enhance the prediction accuracy and PE for these abnormal cases in SCBs significantly and effectively and deliver a valuable insight for satellite navigation.

Performance Similarity Based Method for Enhanced Prediction of Manufacturing Process Performance

2004 ◽  
Author(s):  
Jianbo Liu ◽  
Dragan Djurdjanovic ◽  
Jun Ni ◽  
Jay Lee
Keyword(s):  
Prediction Accuracy ◽  
Linear Prediction ◽  
Moving Average ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Term Prediction ◽  
Arma Modeling ◽  
Prediction Techniques ◽  
Long Term Prediction

Full realization of all potentials in predictive and proactive maintenance highly depends on the accuracy of long-term predictions of the remaining useful life of manufacturing equipment. Parametric linear prediction techniques, such as Autoregressive Moving Average modeling (ARMA), are routinely used to trend and predict future behavior of any time series, but are frequently not appropriate for long-term prediction because of the highly complicated and non-stationary nature of manufacturing processes. In this paper, we propose a novel method that is capable of achieving high long-term prediction accuracy by comparing signatures from two degradation processes using measures of similarity that form a Match Matrix. Through this concept, we can effectively include large amounts of historical information into the prediction of the current degradation process. Similarities with historical records are used to generate possible future distributions of features, which is then used to predict probabilities of failure over time by evaluating overlaps between predicted feature distributions and feature distributions related to unacceptable equipment behavior. Experimental results show that the proposed method results in a significant improvement of long-term prediction accuracy compared with ARMA modeling-based prediction.

Polar Ocean Observation Buoys Seal Failure Research Based on ARMA Theory

2014 ◽  
Vol 577 ◽  
pp. 709-712
Author(s):  
Shi Ming Wang ◽  
Qing Li ◽  
Zhun Ren
Keyword(s):  
Moving Average ◽  
Arma Model ◽  
The Arctic ◽  
Autoregressive Moving Average ◽  
Mechanism Analysis ◽  
Seal Failure ◽  
Status Monitoring ◽  
Seawater Corrosion ◽  
Polar Ocean

Due to the special conditions of the Arctic climate, ocean observation buoys long-term work in harsh environments, such as seawater corrosion, temperature, pressure and other factors to buoy a higher sealing requirements, ocean snorkeling standard sealing system status monitoring and fault diagnosis mechanism analysis using autoregressive-moving average ARMA model for pressure measurement error problem buoy mathematical modeling can be applied to solve the pressure seal failure buoy measurement problems caused errors.

scholarly journals Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6920
Author(s):  
Ines Sansa ◽  
Zina Boussaada ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Solar Radiation ◽  
Hybrid Model ◽  
Moving Average ◽  
Arma Model ◽  
Power Production ◽  
Auto Regressive ◽  
Efficient Prediction ◽  
Prediction Approach ◽  
A Company ◽  
Grid Management

The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. The interest in solar radiation prediction is increasing nowadays so efficient prediction can greatly improve the performance of these different applications. This paper presents a novel solar radiation prediction approach which combines two models, the Auto Regressive Moving Average (ARMA) and the Nonlinear Auto Regressive with eXogenous input (NARX). This choice has been carried out in order to take the advantages of both models to produce better prediction results. The performance of the proposed hybrid model has been validated using a real database corresponding to a company located in Barcelona north. Simulation results have proven the effectiveness of this hybrid model to predict the weekly solar radiation averages. The ARMA model is suitable for small variations of solar radiation while the NARX model is appropriate for large solar radiation fluctuations.

scholarly journals Helsinki Stock Exchange Index during World War One: Statistical Test of Hypotheses on the Basis of Counter-Factual Modeling

2020 ◽  
pp. 55-65
Author(s):  
Daria Anisimova
Keyword(s):  
Stock Exchange ◽  
Moving Average ◽  
Arma Model ◽  
Stationary Time Series ◽  
Economic Factors ◽  
Software Environment ◽  
Autoregressive Moving Average Model ◽  
Test Of Hypotheses ◽  
Improved Model ◽  
Helsinki Stock Exchange

The article proposes an improved model of St. Petersburg Stock Exchange index dynamics and constructs a similar model of Helsinki Stock Exchange index on the basis of published results of a counterfactual model predicting the hypothetical dynamics of St. Petersburg Stock Exchange index after July 1914 under the assumption that there is no war. The author hypothesizes that internal economic factors that determined the downward trend of St. Petersburg Stock Exchange index also influenced the dynamics of Helsinki Stock Exchange index under the assumption that there was no war. To test this hypothesis the author has constructed (in the R software environment) the ARIMA statistical model that is an integrated autoregressive-moving average model which extends the ARMA model for non-stationary time series. The constructed counterfactual models proved that while the influence of pre-war factors remained, the dynamics of both indices did not show similar trends thus suggesting that the Finnish stock market was developing without any noticeable look at St. Petersburg Stock Exchange and inner economic factors of the Russian Empire.

scholarly journals Finance Leasing and ARMA Forecasting: Evidence from Albania

2020 ◽  
Vol 5 (6) ◽  
Author(s):  
Alban Korbi ◽  
Llesh Lleshaj
Keyword(s):  
Small And Medium Enterprises ◽  
Moving Average ◽  
Arma Model ◽  
Medium Term ◽  
Long Run ◽  
Short Run ◽  
Optimal Forecasting ◽  
Econometric Tests ◽  
Medium Enterprises

Ten financial institutions are offering finance leasing-loans in Albania. Even though finance leasing is a potential financing resource for small and medium enterprises in Albania (which are on average 95% of national enterprises), the value of finance leasing is one thousand times smaller than other forms of medium and long-term loans or real estate loans. Developing of finance leasing is a challenge for the progress of the financial sector, and untapped potential as well. Currently, the finance leasing portfolio is dominated by financing for personal vehicles and work-vehicles, therefore diversification of leasing products is an immediate need of consumers. This study analyzes the value of finance leasing in Albania with time series from 2008 to 2020 (with quarterly frequency). The methodology applied for data processing is the co-integration method of finance leasing and other forms of medium-term and long-term financing. Also, the ARMA method is used to forecast the value of finance leasing. We found out that there is no long-run relationship between finance leasing with medium and long-term loans. Therefore, econometric tests suggest optimal forecasting ARMA (1,1) modeling. The parameters of ARMA model are positive statistically significant with autocorrelation AR (1) and negative statistically significant with the moving average MA (1), and forecasting values have a short-run equilibrium with a wide interval.

scholarly journals Current and New Approaches to Predict the Deflections of One-Way Flexural Members with a Focus on Composite Steel Deck Slabs Voided by Circular Tubes

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 421
Author(s):  
Chang-Hwan Lee ◽  
Iman Mansouri ◽  
Jaehoon Bae ◽  
Jaeho Ryu
Keyword(s):  
Structural Function ◽  
Flexural Members ◽  
Long Span ◽  
Deck Slabs ◽  
New Type ◽  
New Perspective ◽  
Flexural Tests ◽  
Prediction Approach ◽  
Steel Deck

A new type of composite voided slab, the TUBEDECK (TD), which utilizes the structural function of profiled steel decks, has recently been proposed. Previous studies have confirmed that the flexural strength of TD slabs can be calculated based on the full composite contribution of the steel deck, but for long-span flexural members, the deflection serviceability requirement is often dominant. Herein, we derived a novel deflection prediction approach using the results of flexural tests on slab specimens, focusing on TD slabs. First, deflection prediction based on modifications of the current code was proposed. Results revealed that TD slabs exhibited smaller long-term deflections and at least 10% longer maximum span lengths than solid slabs, indicating their greater efficiency. Second, a novel rational method was derived for predicting deflections without computing the effective moment of inertia. The ultimate deflections predicted by the proposed method correlated closely with the deflection under maximum bending moments. To calculate immediate deflections, variation functions for the concrete strain at the extreme compression fiber and neutral axis depth were assumed with predictions in good agreement with experiments. The proposed procedure has important implications in highlighting a new perspective on the deflection prediction of reinforced concrete and composite flexural members.

scholarly journals Dietary nitrate and population health: a narrative review of the translational potential of existing laboratory studies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Oliver M. Shannon ◽  
Chris Easton ◽  
Anthony I. Shepherd ◽  
Mario Siervo ◽  
Stephen J. Bailey ◽  
...  
Keyword(s):  
Population Health ◽  
Leafy Vegetables ◽  
Future Research ◽  
Research Approach ◽  
Valuable Insight ◽  
Main Body ◽  
Laboratory Studies ◽  
Potential Health ◽  
Health Promoting

Abstract Background Dietary inorganic nitrate (NO3−) is a polyatomic ion, which is present in large quantities in green leafy vegetables and beetroot, and has attracted considerable attention in recent years as a potential health-promoting dietary compound. Numerous small, well-controlled laboratory studies have reported beneficial health effects of inorganic NO3− consumption on blood pressure, endothelial function, cerebrovascular blood flow, cognitive function, and exercise performance. Translating the findings from small laboratory studies into ‘real-world’ applications requires careful consideration. Main body This article provides a brief overview of the existing empirical evidence basis for the purported health-promoting effects of dietary NO3− consumption. Key areas for future research are then proposed to evaluate whether promising findings observed in small animal and human laboratory studies can effectively translate into clinically relevant improvements in population health. These proposals include: 1) conducting large-scale, longer duration trials with hard clinical endpoints (e.g. cardiovascular disease incidence); 2) exploring the feasibility and acceptability of different strategies to facilitate a prolonged increase in dietary NO3− intake; 3) exploitation of existing cohort studies to explore associations between NO3− intake and health outcomes, a research approach allowing larger samples sizes and longer duration follow up than is feasible in randomised controlled trials; 4) identifying factors which might account for individual differences in the response to inorganic NO3− (e.g. sex, genetics, habitual diet) and could assist with targeted/personalised nutritional interventions; 5) exploring the influence of oral health and medication on the therapeutic potential of NO3− supplementation; and 6) examining potential risk of adverse events with long term high- NO3− diets. Conclusion The salutary effects of dietary NO3− are well established in small, well-controlled laboratory studies. Much less is known about the feasibility and efficacy of long-term dietary NO3− enrichment for promoting health, and the factors which might explain the variable responsiveness to dietary NO3− supplementation between individuals. Future research focussing on the translation of laboratory data will provide valuable insight into the potential applications of dietary NO3− supplementation to improve population health.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

2021 ◽  
pp. 1-17
Author(s):  
Nuzhat Fatema ◽  
H Malik ◽  
Mutia Sobihah Binti Abd Halim
Keyword(s):  
Monte Carlo ◽  
Hybrid Model ◽  
Medical Tourism ◽  
Moving Average ◽  
Hybrid Approach ◽  
Mode Decomposition ◽  
Hybrid Forecasting ◽  
Input Variables ◽  
Intelligent Approach

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

MULTIFRACTAL FLUCTUATIONS OF JIUZHAIGOU TOURISTS BEFORE AND AFTER WENCHUAN EARTHQUAKE

Fractals ◽  
2013 ◽  
Vol 21 (01) ◽  
pp. 1350001 ◽  
Author(s):  
KAI SHI ◽  
WEN-YONG LI ◽  
CHUN-QIONG LIU ◽  
ZHENG-WEN HUANG
Keyword(s):  
Wenchuan Earthquake ◽  
Moving Average ◽  
Long Term Memory ◽  
Short Term ◽  
Dynamical Mechanism ◽  
Before And After ◽  
Previous State ◽  
Multifractal Behavior ◽  
Multifractal Nature

In this work, multifractal methods have been successfully used to characterize the temporal fluctuations of daily Jiuzhai Valley domestic and foreign tourists before and after Wenchuan earthquake in China. We used multifractal detrending moving average method (MF-DMA). It showed that Jiuzhai Valley tourism markets are characterized by long-term memory and multifractal nature in. Moreover, the major sources of multifractality are studied. Based on the concept of sliding window, the time evolutions of the multifractal behavior of domestic and foreign tourists were analyzed and the influence of Wenchuan earthquake on Jiuzhai Valley tourism system dynamics were evaluated quantitatively. The study indicates that the inherent dynamical mechanism of Jiuzhai Valley tourism system has not been fundamentally changed from long views, although Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake. Jiuzhai Valley tourism system has the ability to restore to its previous state in the short term.

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