84 research outputs found
Capturing Evolution Genes for Time Series Data
The modeling of time series is becoming increasingly critical in a wide
variety of applications. Overall, data evolves by following different patterns,
which are generally caused by different user behaviors. Given a time series, we
define the evolution gene to capture the latent user behaviors and to describe
how the behaviors lead to the generation of time series. In particular, we
propose a uniform framework that recognizes different evolution genes of
segments by learning a classifier, and adopt an adversarial generator to
implement the evolution gene by estimating the segments' distribution.
Experimental results based on a synthetic dataset and five real-world datasets
show that our approach can not only achieve a good prediction results (e.g.,
averagely +10.56% in terms of F1), but is also able to provide explanations of
the results.Comment: a preprint version. arXiv admin note: text overlap with
arXiv:1703.10155 by other author
An industry case of large-scale demand forecasting of hierarchical components
Demand forecasting of hierarchical components is essential in manufacturing.
However, its discussion in the machine-learning literature has been limited,
and judgemental forecasts remain pervasive in the industry. Demand planners
require easy-to-understand tools capable of delivering state-of-the-art
results. This work presents an industry case of demand forecasting at one of
the largest manufacturers of electronics in the world. It seeks to support
practitioners with five contributions: (1) A benchmark of fourteen demand
forecast methods applied to a relevant data set, (2) A data transformation
technique yielding comparable results with state of the art, (3) An alternative
to ARIMA based on matrix factorization, (4) A model selection technique based
on topological data analysis for time series and (5) A novel data set.
Organizations seeking to up-skill existing personnel and increase forecast
accuracy will find value in this work
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
Missing value problem in spatiotemporal traffic data has long been a
challenging topic, in particular for large-scale and high-dimensional data with
complex missing mechanisms and diverse degrees of missingness. Recent studies
based on tensor nuclear norm have demonstrated the superiority of tensor
learning in imputation tasks by effectively characterizing the complex
correlations/dependencies in spatiotemporal data. However, despite the
promising results, these approaches do not scale well to large data tensors. In
this paper, we focus on addressing the missing data imputation problem for
large-scale spatiotemporal traffic data. To achieve both high accuracy and
efficiency, we develop a scalable tensor learning model -- Low-Tubal-Rank
Smoothing Tensor Completion (LSTC-Tubal) -- based on the existing framework of
Low-Rank Tensor Completion, which is well-suited for spatiotemporal traffic
data that is characterized by multidimensional structure of location
time of day day. In particular, the proposed LSTC-Tubal model involves
a scalable tensor nuclear norm minimization scheme by integrating linear
unitary transformation. Therefore, tensor nuclear norm minimization can be
solved by singular value thresholding on the transformed matrix of each day
while the day-to-day correlation can be effectively preserved by the unitary
transform matrix. We compare LSTC-Tubal with state-of-the-art baseline models,
and find that LSTC-Tubal can achieve competitive accuracy with a significantly
lower computational cost. In addition, the LSTC-Tubal will also benefit other
tasks in modeling large-scale spatiotemporal traffic data, such as
network-level traffic forecasting
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