ArticleEntropy (Basel, Switzerland)2020
Time Series Complexities and Their Relationship to Forecasting Performance.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Explicit Future Pattern-Enhanced Multivariate Time Series Forecasting.Entropy (Basel, Switzerland) · 2026Article
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?PLoS computational biology · 2026Article
- Entropy-Guided Regime Switching for Railway Passenger Flow Forecasting: An Adaptive EA-ARIMA-Informer Framework.Entropy (Basel, Switzerland) · 2026Article
- KALFormer: Knowledge-augmented attention learning for long-term time series forecasting with transformer.PloS one · 2026Article
- Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation.Entropy (Basel, Switzerland) · 2025Article
- AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets.Sensors (Basel, Switzerland) · 2023Article
- Multivariate Time Series Information Bottleneck.Entropy (Basel, Switzerland) · 2023Article
- Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.Entropy (Basel, Switzerland) · 2023Article
- Entropy Application for Forecasting.Entropy (Basel, Switzerland) · 2020Article
- A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
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Abstract
Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually constrains the forecasting method performance to specific regions; where the logarithm of its metric error is poorer, the Complexity based on the emergence and self-organization is maximal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.