TSViz: Demystification of Deep Learning Models for Time-Series Analysis
This paper presents a novel framework for the demystification of convolutional deep learning models for time-series analysis. This is a step toward making informed/explainable decisions in the domain of time series, powered by deep learning. There have been numerous efforts to increase the interpret...
Үндсэн зохиолчид: | Shoaib Ahmed Siddiqui, Dominique Mercier, Mohsin Munir, Andreas Dengel, Sheraz Ahmed |
---|---|
Формат: | Өгүүллэг |
Хэл сонгох: | English |
Хэвлэсэн: |
IEEE
2019-01-01
|
Цуврал: | IEEE Access |
Нөхцлүүд: | |
Онлайн хандалт: | https://ieeexplore.ieee.org/document/8695734/ |
Ижил төстэй зүйлс
Ижил төстэй зүйлс
-
TSInsight: A Local-Global Attribution Framework for Interpretability in Time Series Data
-н: Shoaib Ahmed Siddiqui, зэрэг
Хэвлэсэн: (2021-11-01) -
TimeREISE: Time Series Randomized Evolving Input Sample Explanation
-н: Dominique Mercier, зэрэг
Хэвлэсэн: (2022-05-01) -
Linear Model and Gradient Feature Elimination Algorithm Based on Seasonal Decomposition for Time Series Forecasting
-н: Sheng-Tzong Cheng, зэрэг
Хэвлэсэн: (2025-03-01) -
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
-н: Mohsin Munir, зэрэг
Хэвлэсэн: (2019-01-01) -
Random Noise vs. State-of-the-Art Probabilistic Forecasting Methods: A Case Study on CRPS-Sum Discrimination Ability
-н: Alireza Koochali, зэрэг
Хэвлэсэн: (2022-05-01)