OnlineSTL: Scaling Time Series Decomposition by 100x

Mishra, Abhinav; Sriharsha, Ram; Zhong, Sichen

doi:10.48550/arxiv.2107.09110

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…The decomposition function, based on addition and multiplication, is shown in Equations ( 1) and (2), respectively [30]. It is important to note that there is a delay in the real-time trend decomposition process, as this involves the utilization of past data at each time step (using non-symmetric filters) to calculate the trend [31].…”

Section: Time Series Decompositionmentioning

confidence: 99%

“…However, when a large window size, such as 500, is selected, the global trends and long-term changes can be captured. Subsequently, each univariate time series is decomposed into two compo- Furthermore, a simple moving average is employed to capture the trend component [31]. This technique utilizes a window size to characterize the trend component.…”

Section: Reducing the Complexity Of Datamentioning

confidence: 99%

See 1 more Smart Citation

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Dabaghi-Zarandi,

Behjat,

Gauvin

et al. 2024

Energies

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An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC’s condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC’s condition.

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Section: Time Series Decompositionmentioning

confidence: 99%

Section: Reducing the Complexity Of Datamentioning

confidence: 99%

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Dabaghi-Zarandi,

Behjat,

Gauvin

et al. 2024

Energies

View full text Add to dashboard Cite

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“…Time series decomposition [18][19][20][21][22][23][24][25][26] aims to decompose a time series into its components structurally and interpretably. These components typically include the trend, seasonal, and residual components.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Portfolio Performance through Financial Time-Series Decomposition-Based Variational Encoder-Decoder Data Augmentation

Kalina,

Lee,

2024

Symmetry

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The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial time series data. However, several issues arise with historical financial time series data, making it challenging to train models effectively to achieve the portfolio diversification objective: an insufficient amount of training data and the uncertainty deficiency problem, wherein the uncertainty that existed in the past is not visible in the present. Insufficient datasets, characterized by small data size, result in information asymmetry and compromise portfolio performance. This limitation underscores the importance of adopting a pattern-centric data augmentation approach, capable of unveiling hidden patterns and structures within the financial time series data. To address these challenges, this paper introduces the financial time series decomposition-based variational encoder-decoder (FED) method to augment financial time series data, overcoming the limitations of insufficient training data and providing a more realistic and dynamic simulation of the financial market environment. By decomposing the data into distinct components, such as trend, dispersion, and residual, FED leverages pattern-centric data augmentation within the financial time series data. In the environment generated using the FED method, this paper proposes a two-class portfolio diversification, called FED2Port. It integrates stochastic elements into the reward function, enabling a reinforcement learning algorithm to learn from a comprehensive spectrum of financial market uncertainties. The experimental results demonstrate that the proposed model significantly enhances portfolio performance.

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OnlineSTL: Scaling Time Series Decomposition by 100x

Cited by 2 publications

References 21 publications

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Enhancing Portfolio Performance through Financial Time-Series Decomposition-Based Variational Encoder-Decoder Data Augmentation

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