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时序模型深度学习方法综述

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A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling

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http://www.diva-portal.org

This is version of a paper published in Pattern Recognition Letters.

Citation for the original published paper (version of record):

Längkvist, M., Karlsson, L., Loutfi, A. (2014)

A review of unsupervised feature learning and deep learning for time-series modeling.

Pattern Recognition Letters, 42(1): 11-24

http://dx.doi.org/10.1016/j.patrec.2014.01.008

Access to the published version may require subscription.

N.B. When citing this work, cite the original published paper.

Permanent link to this version:

http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-34597

A Review of Unsupervised Feature Learning and Deep

Learning for Time-Series Modeling

Martin Längkvist

∗

, Lars Karlsson

, Amy Lout

Applied Autonomous Sensor Systems, School of Science and Technology, Örebro

University, SE-701 82, Örebro, Sweden

Abstract

This paper gives a review of the recent developments in deep learning and un-

supervised feature learning for time-series problems. While these techniques

have shown promise for modeling static data, such as computer vision, ap-

plying them to time-series data is gaining increasing attention. This paper

overviews the particular challenges present in time-series data and provides a

review of the works that have either applied time-series data to unsupervised

feature learning algorithms or alternatively have contributed to modications

of feature learning algorithms to take into account the challenges present in

time-series data.

Keywords:

time-series, unsupervised feature learning, deep learning

1. Introduction and Background

Time is a natural element that is always present when the human brain

is learning tasks like language, vision and motion. Most real-world data

has a temporal component, whether it is measurements of natural processes

∗

Corresponding author

Email addresses:

martin.langkvist@oru.se

(Martin Längkvist),

lars.karlsson@oru.se

(Lars Karlsson),

amy.loutfi@oru.se

(Amy Lout)

Preprint submitted to Pattern Recognition Letters March 24, 2014

(weather, sound waves) or man-made (stock market, robotics). Analysis of

time-series data has been the subject of active research for decades (Keogh

and Kasetty, 2002; Dietterich, 2002) and is considered by Yang and Wu

(2006) as one of the top 10 challenging problems in data mining due to

its unique properties. Traditional approaches for modeling sequential data

include the estimation of parameters from an assumed time-series model,

such as autoregressive models (Lütkepohl, 2005) and Linear Dynamical Sys-

tems (LDS) (Luenberger, 1979), and the popular Hidden Markov Model

(HMM) (Rabiner and Juang, 1986). The estimated parameters can then

be used as features in a classier to perform classication. However, more

complex, high-dimensional, and noisy real-world time-series data cannot be

described with analytical equations with parameters to solve since the dy-

namics are either too complex or unknown (Taylor, 2009) and traditional

shallow methods, which contain only a small number of non-linear opera-

tions, do not have the capacity to accurately model such complex data.

In order to better model complex real-world data, one approach is to

develop robust features that capture the relevant information. However, de-

veloping domain-specic features for each task is expensive, time-consuming,

and requires expertise of the data. The alternative is to use unsupervised

feature learning (Bengio and LeCun, 2007; Bengio et al., 2012; Erhan et al.,

2010) in order to learn a layer of feature representations from unlabeled data.

This has the advantage that the unlabeled data, which is plentiful and easy

to obtain, is utilized and that the features are learned from the data instead

of being hand-crafted. Another benet is that these layers of feature repre-

sentations can be stacked to create deep networks, which are more capable

of modeling complex structures in the data. Deep networks have been used

to achieve state-of-the-art results on a number of benchmark data sets and

for solving dicult AI tasks. However, much focus in the feature learning

community has been on developing models for static data and not so much

on time-series data.

In this paper we review the variety of feature learning algorithms that

has been developed to explicitly capture temporal relationships as well as the

various time-series problems that they have been used on. The properties of

time-series data will be discussed in Section 2 followed by an introduction to

unsupervised feature learning and deep learning in Section 3. An overview

of some common time-series problems and previous work using deep learning

is given in Section 4. Finally, conclusions are given in Section 5.

2. Properties of time-series data

Time-series data consists of sampled data points taken from a continuous,

real-valued process over time. There are a number of characteristics of time-

series data that make it dierent from other types of data.

Firstly, the sampled time-series data often contain much noise and have

high dimensionality. To deal with this, signal processing techniques such

as dimensionality reduction techniques, wavelet analysis or ltering can be

applied to remove some of the noise and reduce the dimensionality. The use

of feature extraction has a number of advantages (Nanopoulos et al., 2001).

However, valuable information could be lost and the choice of features and

signal processing techniques may require expertise of the data.

The second characteristics of time-series data is that it is not certain

that there are enough information available to understand the process. For

example, in electronic nose data, where an array of sensors with various

selectivity for a number of gases are combined to identify a particular smell,

there is no guarantee that the selection of sensors actually are able to identify

the target odour. In nancial data when observing a single stock, which only

measures a small aspect of a complex system, there is most likely not enough

information in order to predict the future (Fama, 1965).

Further, time-series have an explicit dependency on the time variable.

Given an input

x(t)

at time

, the model predicts

y(t)

, but an identical input

at a later time could be associated with a dierent prediction. To solve this

problem, the model either has to include more data input from the past or

must have a memory of past inputs. For long-term dependencies the rst ap-

proach could make the input size too large for the model to handle. Another

challenge is that the length of the time-dependencies could be unknown.

Many time-series are also non-stationary, meaning that the characteristics

of the data, such as mean, variance, and frequency, changes over time. For

some time-series data, the change in frequency is so relevant to the task that it

is more benecial to work in the frequency-domain than in the time-domain.

Finally, there is a dierence between time-series data and other types of

data when it comes to invariance. In other domains, for example computer

vision, it is important to have features that are invariant to translations,

rotations, and scale. Most features used for time-series need to be invariant

to translations in time.

In conclusion, time-series data is high-dimensional and complex with

unique properties that make them challenging to analyze and model. There

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