DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data

05/05/2022
by   Asadullah Hill Galib, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/13/2018

Impact of Data Normalization on Deep Neural Network for Time Series Forecasting

For the last few years it has been observed that the Deep Neural Network...
07/14/2021

Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network

An impact of climate change is the increase in frequency and intensity o...
06/06/2021

Distributed Learning and its Application for Time-Series Prediction

Extreme events are occurrences whose magnitude and potential cause exten...
12/19/2020

Functional time series forecasting of extreme values

We consider forecasting functional time series of extreme values within ...
05/23/2022

Forecasting of Non-Stationary Sales Time Series Using Deep Learning

The paper describes the deep learning approach for forecasting non-stati...
03/27/2020

Financial Time Series Representation Learning

This paper addresses the difficulty of forecasting multiple financial ti...
02/08/2018

TSViz: Demystification of Deep Learning Models for Time-Series Analysis

This paper presents a novel framework for demystification of convolution...