2 research outputs found
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting
Spatial time series forecasting problems arise in a broad range of
applications, such as environmental and transportation problems. These problems
are challenging because of the existence of specific spatial, short-term and
long-term patterns, and the curse of dimensionality. In this paper, we propose
a deep neural network framework for large-scale spatial time series forecasting
problems. We explicitly designed the neural network architecture for capturing
various types of patterns. In preprocessing, a time series decomposition method
is applied to separately feed short-term, long-term and spatial patterns into
different components of a neural network. A fuzzy clustering method finds
cluster of neighboring time series based on similarity of time series
residuals; as they can be meaningful short-term patterns for spatial time
series. In neural network architecture, each kernel of a multi-kernel
convolution layer is applied to a cluster of time series to extract short-term
features in neighboring areas. The output of convolution layer is concatenated
by trends and followed by convolution-LSTM layer to capture long-term patterns
in larger regional areas. To make a robust prediction when faced with missing
data, an unsupervised pretrained denoising autoencoder reconstructs the output
of the model in a fine-tuning step. The experimental results illustrate the
model outperforms baseline and state of the art models in a traffic flow
prediction dataset
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction
Spatio-temporal forecasting is an open research field whose interest is
growing exponentially. In this work we focus on creating a complex deep neural
framework for spatio-temporal traffic forecasting with comparatively very good
performance and that shows to be adaptable over several spatio-temporal
conditions while remaining easy to understand and interpret. Our proposal is
based on an interpretable attention-based neural network in which several
modules are combined in order to capture key spatio-temporal time series
components. Through extensive experimentation, we show how the results of our
approach are stable and better than those of other state-of-the-art
alternatives.Comment: 16 pages, 14 figure