Abstract: Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based transformer models, which had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the website of University of California, Irvine (UCI), which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Absolute Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.
Abstract: This paper presents and benchmarks a number of
end-to-end Deep Learning based models for metaphor detection in
Greek. We combine Convolutional Neural Networks and Recurrent
Neural Networks with representation learning to bear on the metaphor
detection problem for the Greek language. The models presented
achieve exceptional accuracy scores, significantly improving the
previous state-of-the-art results, which had already achieved accuracy
0.82. Furthermore, no special preprocessing, feature engineering or
linguistic knowledge is used in this work. The methods presented
achieve accuracy of 0.92 and F-score 0.92 with Convolutional
Neural Networks (CNNs) and bidirectional Long Short Term Memory
networks (LSTMs). Comparable results of 0.91 accuracy and 0.91
F-score are also achieved with bidirectional Gated Recurrent Units
(GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The
models are trained and evaluated only on the basis of training tuples,
the related sentences and their labels. The outcome is a state-of-the-art
collection of metaphor detection models, trained on limited labelled
resources, which can be extended to other languages and similar
tasks.
Abstract: In data-driven prognostic methods, the prediction
accuracy of the estimation for remaining useful life of bearings
mainly depends on the performance of health indicators, which
are usually fused some statistical features extracted from vibrating
signals. However, the existing health indicators have the following
two drawbacks: (1) The differnet ranges of the statistical features
have the different contributions to construct the health indicators,
the expert knowledge is required to extract the features. (2) When
convolutional neural networks are utilized to tackle time-frequency
features of signals, the time-series of signals are not considered.
To overcome these drawbacks, in this study, the method combining
convolutional neural network with gated recurrent unit is proposed to
extract the time-frequency image features. The extracted features are
utilized to construct health indicator and predict remaining useful life
of bearings. First, original signals are converted into time-frequency
images by using continuous wavelet transform so as to form the
original feature sets. Second, with convolutional and pooling layers
of convolutional neural networks, the most sensitive features of
time-frequency images are selected from the original feature sets.
Finally, these selected features are fed into the gated recurrent unit
to construct the health indicator. The results state that the proposed
method shows the enhance performance than the related studies which
have used the same bearing dataset provided by PRONOSTIA.