Time Series Analysis Based on Informer Algorithms: A Survey

Zhu, Qingbo; Han, Jialin; Chai, Kai; Zhao, Cunsheng

doi:10.3390/sym15040951

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…Additionally, they introduced a time series prediction approach that leverages random search techniques to optimize the Informer algorithm model. Addition-ally, to enhance the computational efficiency of long time series, Zhu et al [94] combined the model with CNN. This enhanced the regional performance of Informer and improved its learning capabilities, reducing computational costs and memory usage.…”

Section: Time Series-based Modelingmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

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Section: Time Series-based Modelingmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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“…The Informer model significantly reduces the computational burden of the existing Transformer. Owing to these features, it can efficiently predict data sequences and its superior performance has been demonstrated in various real-world applications [45,46]. In this study, this model was applied in the single-step forecasting experiment.…”

mentioning

confidence: 99%

Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models

Yang,

Kim,

Ryu

et al. 2024

Electronics

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In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers.

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“…Apart from the abovementioned research papers, this Special Issue contains two review papers. They are focused on informer methods used in a time series analysis [24] and the synergies between machine learning and neurorobotics [25] Finally, I would like to congratulate all the authors of these papers on the acceptance of their work to this Special Issue, and I encourage the authors of rejected papers to improve their manuscripts and to then resubmit them to Symmetry.…”

mentioning

confidence: 99%