Indicators of Chemical Stressor Levels Derived from Population Time Series: A Retrospective Modeling Approach

2009 ◽  
pp. 44-44-12
Author(s):  
TG Hallam ◽  
ET Funasaki
Keyword(s):  
Time Series ◽  
Modeling Approach

scholarly journals Simulation Tools for Transport Monitoring Systems in the Mining Industry

2021 ◽  
Vol 278 ◽  
pp. 01017
Author(s):  
Fares Abu-Abed
Keyword(s):  
Time Series ◽  
Computer Modeling ◽  
Time Series Data ◽  
Mining Industry ◽  
Series Data ◽  
Simulation Tools ◽  
Modeling Approach ◽  
Software Interface ◽  
Measurement Methodology ◽  
Efficient Data

This paper presents a computer modeling approach for monitoring a fleet of mining machines based on a software solution for traffic modeling. Computer simulations can help reduce prototyping costs and reduce the risk of initial launch failure by analyzing and tuning a prototype to test the most appropriate options. Using a computer modeling approach, we show in the first part of the article that the resulting vehicle monitoring metrics can be tested during the modeling process, instead of adding equipment to vehicles during the prototyping phase. Using real equipment in the prototype phase increases fleet downtime and decreases productivity. Using modern solutions for storing time series, we show how easy it is to analyze the data obtained as a result of modeling. In the second part of the article, we propose a workflow for integrating SUMO with a time series data warehouse through a software interface (API) called TraCI, which allows you to aggregate and visualize vehicle fleet data over time. At the end of this work, we discuss the measurement methodology and propose a potential solution for efficient data transmission.

A modeling approach to financial time series based on market microstructure model with jumps

2015 ◽  
Vol 29 ◽  
pp. 40-51 ◽  
Author(s):  
Hui Peng ◽  
Genshiro Kitagawa ◽  
Yoshiyasu Tamura ◽  
Yanhui Xi ◽  
Yemei Qin ◽  
...  
Keyword(s):  
Time Series ◽  
Market Microstructure ◽  
Financial Time Series ◽  
Financial Time ◽  
Modeling Approach ◽  
Microstructure Model

Alternative approaches to the prediction of electricity prices

2017 ◽  
Vol 11 (1) ◽  
pp. 3-27 ◽  
Author(s):  
Pawel D. Domanski ◽  
Mateusz Gintrowski
Keyword(s):  
Time Series ◽  
Electricity Market ◽  
Time Series Prediction ◽  
Structure Design ◽  
Processing Unit ◽  
Generation Process ◽  
Electricity Prices ◽  
Content Type ◽  
Modeling Approach ◽  
Alternative Approaches

Purpose This paper aims to present the results of the comparison between different approaches to the prediction of electricity prices. It is well-known that the properties of the data generation process may prefer some modeling methods over the others. The data having an origin in social or market processes are characterized by unexpectedly wide realization space resulting in the existence of the long tails in the probabilistic density function. These data may not be easy in time series prediction using standard approaches based on the normal distribution assumptions. The electricity prices on the deregulated market fall into this category. Design/methodology/approach The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate interpretation of results is supported with the statistical data analysis and data conditioning. These algorithms have been applied to the problem of the energy price prediction on the deregulated electricity market with data from Polish and Austrian energy stock exchanges. Findings The first outcome of the analysis is that there are several situations in the task of time series prediction, when standard modeling approach based on the assumption that each change is independent of the last following random Gaussian bell pattern may not be a true. In this paper, such a case was considered: price data from energy markets. Electricity prices data are biased by the human nature. It is shown that more relevant for data properties was Cauchy probabilistic distribution. Results have shown that alternative approaches may be used and prediction for both data memory-based approach resulted in the best performance. Research limitations/implications “Personalization” of the model is crucial aspect in the whole methodology. All available knowledge should be used on the forecasted phenomenon and incorporate it into the model. In case of the memory-based modeling, it is a specific design of the history searching routine that uses the understanding of the process features. Importance should shift toward methodology structure design and algorithm customization and then to parameter estimation. Such modeling approach may be more descriptive for the user enabling understanding of the process and further iterative improvement in a continuous striving for perfection. Practical implications Memory-based modeling can be practically applied. These models have large potential that is worth to be exploited. One disadvantage of this modeling approach is large calculation effort connected with a need of constant evaluation of large data sets. It was shown that a graphics processing unit (GPU) approach through parallel calculation on the graphical cards can improve it dramatically. Social implications The modeling of the electricity prices has big impact of the daily operation of the electricity traders and distributors. From one side, appropriate modeling can improve performance mitigating risks associated with the process. Thus, the end users should receive higher quality of services ultimately with lower prices and minimized risk of the energy loss incidents. Originality/value The use of the alternative approaches, such as memory-based reasoning or fractals, is very rare in the field of the electricity price forecasting. Thus, it gives a new impact for further research enabling development of better solutions incorporating all available process knowledge and customized hybrid algorithms.

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