计算机科学
模式识别(心理学)
人工智能
格拉米安矩阵
原始数据
插补(统计学)
双射
卷积神经网络
深度学习
时间序列
数据挖掘
机器学习
缺少数据
数学
程序设计语言
特征向量
物理
组合数学
量子力学
作者
Zhiguang Wang,Tim Oates
出处
期刊:Cornell University - arXiv
日期:2015-07-25
卷期号:: 3939-3945
被引量:27
摘要
Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/ Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time series classification and imputation. We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images. Our approaches achieve highly competitive results when compared to nine of the current best time series classification approaches. Inspired by the bijection property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on the GASF images of four standard and one synthesized compound dataset. The imputation MSE on test data is reduced by 12.18%-48.02% when compared to using the raw data. An analysis of the features and weights learned via tiled CNNs and DAs explains why the approaches work.
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