Self-learning of multivariate time series using perceptually important points

Lintonen, Timo; Räty, Tomi

doi:10.1109/jas.2019.1911777

Cited by 10 publications

(2 citation statements)

References 36 publications

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“…Bi [14] et al proposed a time series algorithm suitable for single-factor distributed version to predict the workload of the next stage. Lintonen and Raty [15] proposed a new selflearning stopping criterion for multivariate time series which is a positive-unlabeled (PU) learning case. Liu [16] et al improved the accuracy of pavement temperature prediction by using a gradient boosting extreme learning machine (GBELM).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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“…The definition of perceptually important points was first introduced in reference [36]. The PIPs algorithm can retain the key turning points in the time series, and its ability to capture the critical points in the time series has been verified in the time series segmentation and pattern recognition [37][38][39]. Interestingly, PIPs have been widely used in the research of stock time series.…”

Section: Perceptually Important Pointsmentioning

confidence: 99%

PFC: A Novel Perceptual Features-Based Framework for Time Series Classification

Wang

Liang

et al. 2021

Entropy

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Time series classification (TSC) is a significant problem in data mining with several applications in different domains. Mining different distinguishing features is the primary method. One promising method is algorithms based on the morphological structure of time series, which are interpretable and accurate. However, existing structural feature-based algorithms, such as time series forest (TSF) and shapelet traverse, all features through many random combinations, which means that a lot of training time and computing resources are required to filter meaningless features, important distinguishing information will be ignored. To overcome this problem, in this paper, we propose a perceptual features-based framework for TSC. We are inspired by how humans observe time series and realize that there are usually only a few essential points that need to be remembered for a time series. Although the complex time series has a lot of details, a small number of data points is enough to describe the shape of the entire sample. First, we use the improved perceptually important points (PIPs) to extract key points and use them as the basis for time series segmentation to obtain a combination of interval-level and point-level features. Secondly, we propose a framework to explore the effects of perceptual structural features combined with decision trees (DT), random forests (RF), and gradient boosting decision trees (GBDT) on TSC. The experimental results on the UCR datasets show that our work has achieved leading accuracy, which is instructive for follow-up research.

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SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

Zhang

Chen

et al. 2021

Transportmetrica B: Transport Dynamics

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Self-learning of multivariate time series using perceptually important points

Cited by 10 publications

References 36 publications

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

PFC: A Novel Perceptual Features-Based Framework for Time Series Classification

SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

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