Examination of errors of table integration in flamelet/progress variable modeling of a turbulent non-premixed jet flame

Pant, Tejas; Han, Chao; Wang, Haifeng

doi:10.1016/j.apm.2019.03.016

Cited by 20 publications

(3 citation statements)

References 38 publications

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“…Each model tends to have its own advantages and limitations for different applications [24]. Combining different models and retaining the advantages of each is the basic idea of the combination approach [25], [26]. To improve forecasting accuracy, the adoption of a hybrid model has become common [27].…”

Section: For Parametric Techniques Several Methods Have Been Used Tomentioning

confidence: 99%

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Wang

Cheng³

et al. 2020

IEEE Access

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As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.

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Section: For Parametric Techniques Several Methods Have Been Used Tomentioning

confidence: 99%

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Wang

Cheng³

et al. 2020

IEEE Access

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“…Each model constituting a hybrid model has its own set of advantages and disadvantages. The key idea behind hybrid modeling [32], [33] is to combine multiple models while retaining each's benefits. These models have shown promising results in addressing forecasting challenges.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

Dedeturk,

Adanur Dedeturk,

Akbaş

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Forecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity of tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the Covid-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 are used. Different estimation models including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and LongTerm Short Memory are applied and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.

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“…The use of MS models has been tested and suggested in several fields of research in economics, finance, and even meteorology or energy (turbine) engineering [10,11,35,36]. The first test was the estimation of the probabilities that a given country is in a recession regime (k = 2) or expansion regime (k = 1) at t + n [12,13] and, closely related, these models were used to test the potential presence of common changes in economic cycles [13,[15][16][17].…”

Section: Review Of the Previous Literature That Motivates Our Testsmentioning

confidence: 99%

A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

2019

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In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.

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Examination of errors of table integration in flamelet/progress variable modeling of a turbulent non-premixed jet flame

Cited by 20 publications

References 38 publications

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

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