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In a series resistor-inductor (R-L) circuit, closing the switch at the start of the time period simulates a three-phase short circuit, a fault condition where all three phases of an unloaded synchronous machine are short-circuited. When there is no fault impedance and no initial current, the initial voltage is determined by the phase angle of the source voltage.
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Connector based short time series prediction.

Wenjuan Gao1,2, Cirong Li1, Siyu Dong3

  • 1School of Business and Management, Jilin University, No. 2699 Str. Qianjin, Changchun, 130012, Prov. Jilin, China.

Scientific Reports
|February 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces novel connectors to merge short time series for improved prediction models. These methods, including linear interpolation and random vibration, enhance Empirical Mode Decomposition (EMD) analysis of combined data.

Keywords:
ConnectorEmpirical mode decompositionShort time series prediction

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Area of Science:

  • Time series analysis
  • Signal processing
  • Machine learning

Background:

  • Classical prediction models struggle with short time series due to incomplete pattern information.
  • Direct concatenation of short series can introduce significant deviations and disrupt data regularity.

Purpose of the Study:

  • To propose a multi-series prediction model that effectively combines short time series.
  • To improve the performance of prediction models by addressing the limitations of direct concatenation.

Main Methods:

  • Dataset normalization.
  • Introduction of two connector types: linear interpolation and linear interpolation with random vibration (LRV).
  • Application of Empirical Mode Decomposition (EMD) to the concatenated series.

Main Results:

  • The proposed connectors facilitate EMD in decomposing sub-sequences that better reflect original short series characteristics.
  • LRV connectors are effective for periodic multi-series data.
  • Linear interpolation connectors are suitable for non-periodic short series.

Conclusions:

  • The developed multi-series prediction model, utilizing connectors and EMD, offers a superior approach to handling short time series data.
  • The choice of connector depends on the periodicity of the time series, offering flexibility in application.