A Novel Approach to the Similarity Analysis of Multivariate Time Series and Its Application in Hydrological Data Mining

2008 ◽  
Author(s):  
Zhu Yuelong ◽  
Li Shijin ◽  
Wan Dingsheng ◽  
Zhang Xiaohua
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Multivariate Time Series ◽  
Similarity Analysis ◽  
Hydrological Data ◽  
Novel Approach

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

Financial Data Mining Using Flexible ICA-GARCH Models

2010 ◽  
pp. 255-272
Author(s):  
Philip L.H. Yu ◽  
Edmond H.C. Wu ◽  
W.K. Li
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Risk Management ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Independent Component ◽  
Garch Models ◽  
Financial Time

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.

Multivariate Time Series Data Mining in Ship Monitoring Database

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Afshin Abbasi Hoseini ◽  
Sverre Steen
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Series Data ◽  
Specific Information ◽  
Self Organizing Map ◽  
Time Series Data Mining ◽  
Wide Range

A framework is presented for data mining in multivariate time series collected over hours of ship operation to extract vessel states from the data. The measurements made by a ship monitoring system lead to a collection of time-organized in-service data. Usually, these time series datasets are big, complicated, and highly dimensional. The purpose of time-series data mining is to bridge the gap between a massive database and meaningful information hidden behind the data. An important aspect of the framework proposed is selecting relevant variables, eliminating unnecessary information or noises, and extracting the essential features of the problem so that the vessel behavior can be identified reliably. Principal component analysis (PCA) is employed to address the issues of multicollinearity in the data and dimensionality reduction. The data mining approach itself is established on unsupervised data clustering using self-organizing map (SOM) and k-means, and k-nearest neighbors search (k-NNS) for searching and recovering specific information from the database. As a case study, the results are based on onboard monitoring data of the Norwegian University of Science and Technology (NTNU) research vessel, “Gunnerus.” The scope of this work is limited to detecting ship maneuvers. However, it is extendable to a wide range of smart marine applications. As illustrated in the results, this approach is effective in identifying the prior unknown states of the ship with acceptable accuracy.

Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Haiyang Zheng ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Wind Farm ◽  
Multivariate Time Series ◽  
Future Research ◽  
Support Vector ◽  
Time Series Models ◽  
Ramp Rate ◽  
Data Mining Algorithms ◽  
Mining Algorithms

In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided.

scholarly journals A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 121
Author(s):  
Sichen Li ◽  
Mélissa Zacharias ◽  
Jochem Snuverink ◽  
Jaime Coello de Portugal ◽  
Fernando Perez-Cruz ◽  
...  
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Multivariate Time Series ◽  
Proton Accelerator ◽  
Particle Accelerators ◽  
Recurrence Plots ◽  
Time Loss ◽  
Safety Measures ◽  
Novel Approach

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.

scholarly journals Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Narendhar Gugulothu ◽  
Vishnu TV ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
Puneet Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Novel Approach ◽  
Useful Life ◽  
Robust To Noise

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art (Malhotra, TV, et al., 2016) on several metrics.

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