Differences in regression, classification, and multi-task deep learning on pressure time series for range and seabed type

2020 ◽  
Vol 148 (4) ◽  
pp. 2727-2727
Author(s):  
David F. Van Komen ◽  
Kira Howarth ◽  
Tracianne B. Neilsen ◽  
David P. Knobles
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Pressure Time

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

Deep learning models for forecasting aviation demand time series

2021 ◽  
Author(s):  
Andreas Kanavos ◽  
Fotios Kounelis ◽  
Lazaros Iliadis ◽  
Christos Makris
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Models

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals A Deep Learning-Based Framework for Damage Detection with Time Series

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qun Yang ◽  
Dejian Shen ◽  
Wencai Du ◽  
Weijun Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Damage Detection

scholarly journals Aggregating Time Series and Tabular Data in Deep Learning Model for University Students’ GPA Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Harjanto Prabowo ◽  
Alam A. Hidayat ◽  
Tjeng Wawan Cenggoro ◽  
Reza Rahutomo ◽  
Kartika Purwandari ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
University Students ◽  
Learning Model ◽  
Tabular Data ◽  
Deep Learning Model
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