Surrogate Modeling for Explainable Predictive Time Series Corrections
We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series 'base model' is used. 'Explainability' of the correction is provided by fitting the base model again to the data from which the error prediction is removed (subtracted), yielding a difference in the model parameters which can be interpreted. We provide illustrative examples to demonstrate the potential of the method to discover and explain underlying patterns in the data.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Explainable Failure Predictions with RNN Classifiers based on Time Series Data
Given key performance indicators collected with fine granularity as time series, our aim is to predict and explain failures in storage environments. Although explainable predictive modeling based on spiky telemetry data …
Time SeriesTime Series AnalysisA Self-explainable Model of Long Time Series by Extracting Informative Structured Causal Patterns
Explainability is essential for neural networks that model long time series, yet most existing explainable AI methods only produce point-wise importance scores and fail to capture temporal structures such as trends, cycl…
Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations
While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent. This assumption igno…
Multivariate Time Series ForecastingTSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
Time series forecasting, while vital in various applications, often employs complex models that are difficult for humans to understand. Effective explainable AI techniques are crucial to bridging the gap between model pr…
Time SeriesTime Series ForecastingUnivariate Time Series ForecastingNegative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series
Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains. Poisson gamma dynamical systems (PGDSs) are newly-developed methods, which…
ImputationTime Series