LSTM Fully Convolutional Networks for Time Series Classification 学习记录

LSTM Fully Convolutional Networks for Time Series Classification
用于时间序列分类的LSTM+FCN网络（Long short-term Memory+Fully Convolutional Networks)

INTRODUCTION

A plethora of research have been done using feature-based approaches
or methods to extract a set of features that represent time series
patterns.Bag-of-Words (BoW) , Bag-of-features (TSBF) , Bag-of-SFA-Symbols (BOSS) , BOSSVS ,Word ExtrAction for time Series cLassification (WEASEL), have obtained promising results in the field. Bag-of-words
quantizes the extracted features and feeds the BoW into a classifier. TSBF extracts multiple subsequences of random local information, which a supervised learner condenses into a cookbook used to predict time series labels. BOSS introduces a combination of a distance based classifier and histograms.The histograms represent substructures of a time series that
are created using a symbolic Fourier approximation. BOSSVS extends this method by proposing a vector space model to reduce time complexity while maintaining performance.WEASEL converts time series into feature vectors using a sliding window. Machine learning algorithms utilize these feature vectors to detect and classify the time series. All these classifiers require heavy feature extraction and feature engineering.

许多研究使用基于特征的方式去提取一组代表时间序列参数的特征。
Bag-of-words量化提取的特征，并将 BoW 馈送到分类器中。
TSBF 提取随机的多个子序列本地信息，受监督的学习者将其浓缩为
用于预测时间序列标签的说明书。
BOSS引入了基于距离的分类器和直方图的组合。
直方图表示时间序列的子结构，这些子结构使用标准傅里叶近似创建。
BOSSVS通过提出一种向量空间模型来扩展这种方法，以降低时间复杂性，同时保持性能。
WEASEL使用滑动窗口将时间序列转换为特征向量。机器学习算法利用这些特征向量来检测和分类时间序列。所有这些分类器都需要大量的特征提取和特征工程。

Ensemble algorithms also yield state-of-the-art performance with time
series classification problems. Three of the most successful ensemble
algorithms that integrate various features of a time series are
Elastic Ensemble (PROP) , a model that integrates 11 time series
classifiers using a weighted ensemble method, Shapelet ensemble (SE)
[8], a model that applies a heterogeneous ensemble onto transformed
shapelets, and a flat collective of transform based ensembles (COTE)
[8], a model that fuses 35 various classifiers into a single
classifier.

集成算法也可提供最先进的性能去处理时间序列分类问题。
其中三个最成功的集成算法使用了大量的时间序列特征。
PROP，一个使用加权集成算法的融合了11个时间序列分类器的模型。
SE，一个使用各种各样的集成去转换形状的模型。

This paper proposes two deep learning models for end to-end time
series classification. The proposed models do not require heavy
preprocessing on the data or feature engineering. Both the models are
tested on all 85 UCR time series benchmarks and outperform most of the
state-of-the-art