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Related Concept Videos

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Jan 6, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Improving the forecasting performance of temporal hierarchies.

Evangelos Spiliotis1, Fotios Petropoulos2, Vassilios Assimakopoulos1

  • 1Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece.

Plos One
|October 4, 2019
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Summary

This study enhances temporal hierarchies for more accurate forecasting by combining multiple methods, adjusting bias, and avoiding seasonal shrinkage. These strategies significantly improve forecast accuracy and reduce bias across different planning horizons.

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Area of Science:

  • Time series analysis
  • Forecasting methodology
  • Statistical modeling

Background:

  • Temporal hierarchies are effective for multi-horizon forecasting but face limitations tied to base forecast methods and data characteristics.
  • Existing methods often struggle with accuracy and bias, especially when dealing with seasonal data patterns.

Purpose of the Study:

  • To address limitations in temporal hierarchy forecasting.
  • To introduce and evaluate strategies for improving forecast accuracy and reducing bias.
  • To enhance the reliability of forecasts across various planning horizons.

Main Methods:

  • Combining forecasts from multiple forecasting methods.
  • Implementing bias adjustment techniques on base forecasts.
  • Selective application of temporal hierarchies to mitigate seasonal shrinkage effects.
  • Evaluation using monthly data from the M and M3 competitions.

Main Results:

  • The proposed strategies demonstrate significant potential for improving temporal hierarchy performance.
  • Improvements were observed in both forecast accuracy and bias reduction.
  • The strategies can be applied independently or in combination with existing reconciliation methods.

Conclusions:

  • The investigated strategies offer a promising approach to overcome current limitations in temporal hierarchy forecasting.
  • These methods enhance the overall effectiveness and reliability of hierarchical forecasting systems.
  • Further application of these techniques can lead to more robust and accurate multi-horizon forecasts.