DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data
Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) with generalized extreme value (GEV) distribution to forecast the block maximum value of a time series. Implementing such a network is a challenge as the framework must preserve the inter-dependent constraints among the GEV model parameters even when the DNN is initialized. We describe our approach to address this challenge and present an architecture that enables both conditional mean and quantile prediction of the block maxima. The extensive experiments performed on both real-world and synthetic data demonstrated the superiority of DeepExtrema compared to other baseline methods.
Code (1)
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that delive…
Correlated Time Series ForecastingTime SeriesTime Series AnalysisTime Series ForecastingUniversal Time Series Generation with Neural Controlled Differential Equations
Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time-series modelling. While these works focus on discriminative setting…
OFTER: An Online Pipeline for Time Series Forecasting
We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrat…
Dimensionality ReductionTime SeriesTime Series ForecastingTimes2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting
Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time series data often present intricate tem…
energy managementTime SeriesTime Series ForecastingDeep Learning for Time Series Forecasting: Tutorial and Literature Survey
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these metho…
BIG-bench Machine LearningDeep LearningSurveyTime Series+3