2,930,663 research outputs found
Foundations of Sequence-to-Sequence Modeling for Time Series
The availability of large amounts of time series data, paired with the
performance of deep-learning algorithms on a broad class of problems, has
recently led to significant interest in the use of sequence-to-sequence models
for time series forecasting. We provide the first theoretical analysis of this
time series forecasting framework. We include a comparison of
sequence-to-sequence modeling to classical time series models, and as such our
theory can serve as a quantitative guide for practitioners choosing between
different modeling methodologies.Comment: To appear at AISTATS 201
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions
We propose a fully convolutional sequence-to-sequence encoder architecture
with a simple and efficient decoder. Our model improves WER on LibriSpeech
while being an order of magnitude more efficient than a strong RNN baseline.
Key to our approach is a time-depth separable convolution block which
dramatically reduces the number of parameters in the model while keeping the
receptive field large. We also give a stable and efficient beam search
inference procedure which allows us to effectively integrate a language model.
Coupled with a convolutional language model, our time-depth separable
convolution architecture improves by more than 22% relative WER over the best
previously reported sequence-to-sequence results on the noisy LibriSpeech test
set
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
We aim to mine temporal causal sequences that explain observed events
(consequents) in time-series traces. Causal explanations of key events in a
time-series has applications in design debugging, anomaly detection, planning,
root-cause analysis and many more. We make use of decision trees and interval
arithmetic to mine sequences that explain defining events in the time-series.
We propose modified decision tree construction metrics to handle the
non-determinism introduced by the temporal dimension. The mined sequences are
expressed in a readable temporal logic language that is easy to interpret. The
application of the proposed methodology is illustrated through various
examples.Comment: This article appears in the Journal of Artificial Intelligenc
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs
We propose here an extended attention model for sequence-to-sequence
recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time
series. This extended attention model can be deployed on top of any RNN and is
shown to yield state-of-the-art performance for time series forecasting on
several univariate and multivariate time series
Linear-Time Sequence Classification using Restricted Boltzmann Machines
Classification of sequence data is the topic of interest for dynamic Bayesian
models and Recurrent Neural Networks (RNNs). While the former can explicitly
model the temporal dependencies between class variables, the latter have a
capability of learning representations. Several attempts have been made to
improve performance by combining these two approaches or increasing the
processing capability of the hidden units in RNNs. This often results in
complex models with a large number of learning parameters. In this paper, a
compact model is proposed which offers both representation learning and
temporal inference of class variables by rolling Restricted Boltzmann Machines
(RBMs) and class variables over time. We address the key issue of
intractability in this variant of RBMs by optimising a conditional
distribution, instead of a joint distribution. Experiments reported in the
paper on melody modelling and optical character recognition show that the
proposed model can outperform the state-of-the-art. Also, the experimental
results on optical character recognition, part-of-speech tagging and text
chunking demonstrate that our model is comparable to recurrent neural networks
with complex memory gates while requiring far fewer parameters
A time warping approach to multiple sequence alignment
We propose an approach for multiple sequence alignment (MSA) derived from the
dynamic time warping viewpoint and recent techniques of curve synchronization
developed in the context of functional data analysis. Starting from pairwise
alignments of all the sequences (viewed as paths in a certain space), we
construct a median path that represents the MSA we are looking for. We
establish a proof of concept that our method could be an interesting ingredient
to include into refined MSA techniques. We present a simple synthetic
experiment as well as the study of a benchmark dataset, together with
comparisons with 2 widely used MSA softwares
Time-Dependent Representation for Neural Event Sequence Prediction
Existing sequence prediction methods are mostly concerned with
time-independent sequences, in which the actual time span between events is
irrelevant and the distance between events is simply the difference between
their order positions in the sequence. While this time-independent view of
sequences is applicable for data such as natural languages, e.g., dealing with
words in a sentence, it is inappropriate and inefficient for many real world
events that are observed and collected at unequally spaced points of time as
they naturally arise, e.g., when a person goes to a grocery store or makes a
phone call. The time span between events can carry important information about
the sequence dependence of human behaviors. In this work, we propose a set of
methods for using time in sequence prediction. Because neural sequence models
such as RNN are more amenable for handling token-like input, we propose two
methods for time-dependent event representation, based on the intuition on how
time is tokenized in everyday life and previous work on embedding
contextualization. We also introduce two methods for using next event duration
as regularization for training a sequence prediction model. We discuss these
methods based on recurrent neural nets. We evaluate these methods as well as
baseline models on five datasets that resemble a variety of sequence prediction
tasks. The experiments revealed that the proposed methods offer accuracy gain
over baseline models in a range of settings.Comment: 9 pages and 2 pages of reference
Time and Activity Sequence Prediction of Business Process Instances
The ability to know in advance the trend of running process instances, with
respect to different features, such as the expected completion time, would
allow business managers to timely counteract to undesired situations, in order
to prevent losses. Therefore, the ability to accurately predict future features
of running business process instances would be a very helpful aid when managing
processes, especially under service level agreement constraints. However,
making such accurate forecasts is not easy: many factors may influence the
predicted features.
Many approaches have been proposed to cope with this problem but all of them
assume that the underling process is stationary. However, in real cases this
assumption is not always true. In this work we present new methods for
predicting the remaining time of running cases. In particular we propose a
method, assuming process stationarity, which outperforms the state-of-the-art
and two other methods which are able to make predictions even with
non-stationary processes. We also describe an approach able to predict the full
sequence of activities that a running case is going to take. All these methods
are extensively evaluated on two real case studies
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
Analyzing the underlying structure of multiple time-sequences provides
insights into the understanding of social networks and human activities. In
this work, we present the \emph{Bayesian nonparametric Poisson process
allocation} (BaNPPA), a latent-function model for time-sequences, which
automatically infers the number of latent functions. We model the intensity of
each sequence as an infinite mixture of latent functions, each of which is
obtained using a function drawn from a Gaussian process. We show that a
technical challenge for the inference of such mixture models is the
unidentifiability of the weights of the latent functions. We propose to cope
with the issue by regulating the volume of each latent function within a
variational inference algorithm. Our algorithm is computationally efficient and
scales well to large data sets. We demonstrate the usefulness of our proposed
model through experiments on both synthetic and real-world data sets.Comment: Revise the writin
nCk sequences and their difference sequences
A nCk sequence is a sequence of n-bit numbers with k bits set. Given such a
sequence C, the difference sequence D of C is subject to certain regularities
that make it possible to generate D in 2|C| time, and, hence, to generate C in
3|C| time
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