117 research outputs found
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has led to the
development of new techniques for digital pattern classification. Very recently, deep learning (DL)
models have emerged as a powerful solution to approach many machine learning (ML) problems.
In particular, convolutional neural networks (CNNs) are currently the state of the art for many image
classification tasks. While there exist several promising proposals on the application of CNNs to
LULC classification, the validation framework proposed for the comparison of different methods
could be improved with the use of a standard validation procedure for ML based on cross-validation
and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed
architecture and parametrization, to achieve high accuracy on LULC classification over RS data
from different sources such as radar and hyperspectral. We also present a methodology to perform
a rigorous experimental comparison between our proposed DL method and other ML algorithms
such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out
demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance
for all the datasets studied, regardless of their different characteristics.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-
On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data
Object detection using remote sensing data is a key task of the perception systems of
self-driving vehicles. While many generic deep learning architectures have been proposed for this
problem, there is little guidance on their suitability when using them in a particular scenario such
as autonomous driving. In this work, we aim to assess the performance of existing 2D detection
systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the
on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3)
and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions
and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are
trained using transfer learning and compared in terms of both precision and efficiency, with special
attention to the real-time requirements of this context. For the experimental study, we use theWaymo
Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage
detectors, our findings show that two-stage detectors still provide the most robust performance.
Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the
detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff
but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS
detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-RJunta de Andalucía US-1263341Junta de Andalucía P18-RT-277
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
In recent years, deep learning techniques have outperformed traditional
models in many machine learning tasks. Deep neural networks have successfully
been applied to address time series forecasting problems, which is a very
important topic in data mining. They have proved to be an effective solution
given their capacity to automatically learn the temporal dependencies present
in time series. However, selecting the most convenient type of deep neural
network and its parametrization is a complex task that requires considerable
expertise. Therefore, there is a need for deeper studies on the suitability of
all existing architectures for different forecasting tasks. In this work, we
face two main challenges: a comprehensive review of the latest works using deep
learning for time series forecasting; and an experimental study comparing the
performance of the most popular architectures. The comparison involves a
thorough analysis of seven types of deep learning models in terms of accuracy
and efficiency. We evaluate the rankings and distribution of results obtained
with the proposed models under many different architecture configurations and
training hyperparameters. The datasets used comprise more than 50000 time
series divided into 12 different forecasting problems. By training more than
38000 models on these data, we provide the most extensive deep learning study
for time series forecasting. Among all studied models, the results show that
long short-term memory (LSTM) and convolutional networks (CNN) are the best
alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve
comparable performance with less variability of results under different
parameter configurations, while also being more efficient
Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation
Most of the current data sources generate large amounts of
data over time. Renewable energy generation is one example of such data
sources. Machine learning is often applied to forecast time series. Since
data flows are usually large, trends in data may change and learned pat terns might not be optimal in the most recent data. In this paper, we
analyse wind energy generation data extracted from the Sistema de Infor mación del Operador del Sistema (ESIOS) of the Spanish power grid. We
perform a study to evaluate detecting concept drifts to retrain models
and thus improve the quality of forecasting. To this end, we compare the
performance of a linear regression model when it is retrained randomly
and when a concept drift is detected, respectively. Our experiments show
that a concept drift approach improves forecasting between a 7.88% and
a 33.97% depending on the concept drift technique appliedMinisterio de Ciencia e Innovación PID2020-117954RB-C22Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277
Asynchronous dual-pipeline deep learning framework for online data stream classification
Data streaming classification has become an essential task in many fields where real-time decisions have to be made
based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their
incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity
streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness
of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep
learning framework in which training and testing are performed simultaneously in two different processes. The data stream
entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning
model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets
from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods
such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in
performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-
Evaluation of the transformer architecture for univariate time series forecasting
The attention-based Transformer architecture is earning in-
creasing popularity for many machine learning tasks. In this study, we
aim to explore the suitability of Transformers for time series forecasting,
which is a crucial problem in di erent domains. We perform an extensive
experimental study of the Transformer with di erent architecture and
hyper-parameter con gurations over 12 datasets with more than 50,000
time series. The forecasting accuracy and computational e ciency of
Transformers are compared with state-of-the-art deep learning networks
such as LSTM and CNN. The obtained results demonstrate that Trans-
formers can outperform traditional recurrent or convolutional models due
to their capacity to capture long-term dependencies, obtaining the most
accurate forecasts in ve out of twelve datasets. However, Transformers
are generally more di cult to parametrize and show higher variability
of results. In terms of e ciency, Transformer models proved to be less
competitive in inference time and similar to the LSTM in training time.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277
On the performance of deep learning models for time series classification in streaming
Processing data streams arriving at high speed requires the
development of models that can provide fast and accurate predictions.
Although deep neural networks are the state-of-the-art for many machine
learning tasks, their performance in real-time data streaming scenarios
is a research area that has not yet been fully addressed. Nevertheless,
there have been recent efforts to adapt complex deep learning models
for streaming tasks by reducing their processing rate. The design of the
asynchronous dual-pipeline deep learning framework allows to predict
over incoming instances and update the model simultaneously using two
separate layers. The aim of this work is to assess the performance of different
types of deep architectures for data streaming classification using
this framework. We evaluate models such as multi-layer perceptrons, recurrent,
convolutional and temporal convolutional neural networks over
several time-series datasets that are simulated as streams. The obtained
results indicate that convolutional architectures achieve a higher performance
in terms of accuracy and efficiency.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-RJunta de Andalucía US-1263341Junta de Andalucía P18-RT-277
Data streams classification using deep learning under different speeds and drifts
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate
predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in
real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has
been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The
design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the
model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL
architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons,
recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as
streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in
evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms
of accuracy and efficiency, but are also the most sensitive to concept drifts.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C22Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277
Temporal convolutional networks applied to energy-related time series forecasting
Modern energy systems collect high volumes of data that can provide valuable information
about energy consumption. Electric companies can now use historical data to make informed
decisions on energy production by forecasting the expected demand. Many deep learning models
have been proposed to deal with these types of time series forecasting problems. Deep neural
networks, such as recurrent or convolutional, can automatically capture complex patterns in time
series data and provide accurate predictions. In particular, Temporal Convolutional Networks
(TCN) are a specialised architecture that has advantages over recurrent networks for forecasting
tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual
blocks, and can also be more efficient in terms of computation time. In this work, we propose a
TCN-based deep learning model to improve the predictive performance in energy demand forecasting.
Two energy-related time series with data from Spain have been studied: the national electric demand
and the power demand at charging stations for electric vehicles. An extensive experimental study has
been conducted, involving more than 1900 models with different architectures and parametrisations.
The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM)
recurrent networks, which are considered the state-of-the-art in the field.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-RJunta de Andalucía US-1263341Junta de Andalucía P18-RT-277
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