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Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

172
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
172
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

787
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
787
State Space to Transfer Function01:21

State Space to Transfer Function

372
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
372
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

202
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
202
Observational Learning01:12

Observational Learning

493
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
493
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

3.0K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Related Experiment Videos

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series.

Alaa Sagheer1,2, Hala Hamdoun2,3, Hassan Youness3

  • 1College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study introduces a Deep Long Short-Term Memory (DLSTM) auto-encoder (AE) model for hierarchical time series forecasting. The approach enhances forecasting accuracy and coherence across hierarchy levels, outperforming existing methods.

Keywords:
australian tourismauto-encodercoherent forecastdeep long short-term memoryhierarchical time seriespower generation

Related Experiment Videos

Area of Science:

  • Data Science
  • Machine Learning
  • Time Series Analysis

Background:

  • Hierarchical time series data are prevalent in real-world applications, posing challenges for accurate and consistent forecasting.
  • Ensuring forecast consistency across different aggregation levels in hierarchical structures is complex and computationally intensive.

Purpose of the Study:

  • To develop an effective Deep Long Short-Term Memory (DLSTM) auto-encoder (AE) model for hierarchical time series forecasting.
  • To leverage transfer learning to mitigate the computational burden and data requirements of training DLSTM models in hierarchical architectures.

Main Methods:

  • Developed a DLSTM model in an auto-encoder (AE) configuration tailored for hierarchical time series.
  • Implemented a transfer learning strategy, initially training on bottom-level series and then transferring features to upper levels.
  • Evaluated the DLSTM-AE approach against traditional and machine learning methods using energy and tourism datasets.

Main Results:

  • The proposed DLSTM-AE approach achieved superior forecasting accuracy compared to all benchmark methods in both case studies.
  • The model demonstrated an enhanced ability to produce coherent forecasts across all levels of the hierarchy.
  • Transfer learning effectively reduced training time and data dependency for upper-level series.

Conclusions:

  • The DLSTM-AE model with transfer learning offers a powerful and efficient solution for hierarchical time series forecasting.
  • This method significantly improves both accuracy and coherence in forecasting complex hierarchical data.
  • The approach provides a viable alternative to existing methods, particularly for large-scale hierarchical forecasting tasks.