Time Series Forecastability Measures

• URL: http://arxiv.org/abs/2507.13556v1
• Date: Thu, 17 Jul 2025 22:23:51 GMT
• Title: Time Series Forecastability Measures
• Authors: Rui Wang, Steven Klee, Alexis Roos,
• Abstract summary: This paper proposes using two metrics to quantify the forecastability of time series prior to model development.<n>The spectral predictability score evaluates the strength and regularity of frequency components in the time series.<n>The Lyapunov exponents quantify the chaos and stability of the system generating the data.
• Score: 4.136441456697068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes using two metrics to quantify the forecastability of time series prior to model development: the spectral predictability score and the largest Lyapunov exponent. Unlike traditional model evaluation metrics, these measures assess the inherent forecastability characteristics of the data before any forecast attempts. The spectral predictability score evaluates the strength and regularity of frequency components in the time series, whereas the Lyapunov exponents quantify the chaos and stability of the system generating the data. We evaluated the effectiveness of these metrics on both synthetic and real-world time series from the M5 forecast competition dataset. Our results demonstrate that these two metrics can correctly reflect the inherent forecastability of a time series and have a strong correlation with the actual forecast performance of various models. By understanding the inherent forecastability of time series before model training, practitioners can focus their planning efforts on products and supply chain levels that are more forecastable, while setting appropriate expectations or seeking alternative strategies for products with limited forecastability.

Related papers

• Deconfounding Time Series Forecasting [1.5967186772129907]
  Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current observations of variables to predict future outcomes. We propose an enhanced forecasting approach that incorporates representations of latent confounders derived from historical data.
  arXiv  Detail & Related papers  (2024-10-27T12:45:42Z)
• Optimal starting point for time series forecasting [1.9937737230710553]
  We introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting.<n>The proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models.<n> Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset.
  arXiv  Detail & Related papers  (2024-09-25T11:51:00Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• Attention-Based Ensemble Pooling for Time Series Forecasting [55.2480439325792]
  We propose a method for pooling that performs a weighted average over candidate model forecasts. We test this method on two time-series forecasting problems: multi-step forecasting of the dynamics of the non-stationary Lorenz 63 equation, and one-step forecasting of the weekly incident deaths due to COVID-19.
  arXiv  Detail & Related papers  (2023-10-24T22:59:56Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Measuring the Stability of Process Outcome Predictions in Online Settings [4.599862571197789]
  This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring. The framework introduces four performance meta-measures: the frequency of significant performance drops, the magnitude of such drops, the recovery rate, and the volatility of performance. The results demonstrate that these meta-measures facilitate the comparison and selection of predictive models for different risk-taking scenarios.
  arXiv  Detail & Related papers  (2023-10-13T10:37:46Z)
• Counterfactual Explanations for Time Series Forecasting [14.03870816983583]
  We formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF. ForecastCF solves the problem by applying gradient-based perturbations to the original time series. Our results show that ForecastCF outperforms the baseline in terms of counterfactual validity and data manifold closeness.
  arXiv  Detail & Related papers  (2023-10-12T08:51:59Z)
• Performative Time-Series Forecasting [64.03865043422597]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective.<n>We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts.<n>We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves [0.0]
  We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series. We propose using late data fusion, using a stacked ensemble of two forecasting models and two meta-features that prove their predictive power during a preliminary forecasting stage.
  arXiv  Detail & Related papers  (2021-08-19T14:44:46Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.