scholarly journals A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series

2021 ◽  
Vol 1 (3) ◽  
pp. 765-776
Author(s):  
Jianqing Wu ◽  
Bo Du ◽  
Qiang Wu ◽  
Jun Shen ◽  
Luping Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Time Delay ◽  
Dwell Time ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Learning Approaches ◽  
Term Prediction ◽  
Long Term Prediction ◽  
Delay Prediction

In many big cities, train delays are among the most complained-about events by the public. Although various models have been proposed for train delay prediction, prior studies on both primary and secondary train delay prediction are limited in number. Recent advances in deep learning approaches and increasing availability of various data sources has created new opportunities for more efficient and accurate train delay prediction. In this study, we propose a hybrid deep learning solution by integrating long short-term memory (LSTM) and Critical Point Search (CPS). LSTM deals with long-term prediction tasks of trains’ running time and dwell time, while CPS uses predicted values with a nominal timetable to identify primary and secondary delays based on the delay causes, run-time delay, and dwell time delay. To validate the model and analyse its performance, we compare the standard LSTM with the proposed hybrid model. The results demonstrate that new variants outperform the standard LSTM, based on predicting time steps of dwell time feature. The experiment results also showed many irregularities of historical trends, which draws attention for further research.

Deep learning-based long-term prediction of air quality parameters

2021 ◽  
Vol 14 (21) ◽  
Author(s):  
Öznur Begüm Gökçek ◽  
Yeşim Dokuz ◽  
Aslı Bozdağ
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Quality Parameters ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

2020 ◽  
Vol 10 (4) ◽  
pp. 1504 ◽  
Author(s):  
Imam Mustafa Kamal ◽  
Hyerim Bae ◽  
Sim Sunghyun ◽  
Heesung Yun
Keyword(s):  
Deep Learning ◽  
Network Models ◽  
Neural Network Models ◽  
Business Decisions ◽  
Term Prediction ◽  
Short And Long Term ◽  
The Baltic ◽  
Long Term Prediction ◽  
Baltic Dry Index

The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

scholarly journals A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

2021 ◽  
Vol 13 (7) ◽  
pp. 1284
Author(s):  
Weilin Wang ◽  
Wenjing Mao ◽  
Xueli Tong ◽  
Gang Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Temporal Correlations ◽  
Term Prediction ◽  
Long Short Term Memory ◽  
Air Quality Monitoring Stations ◽  
Long Term Prediction

Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

scholarly journals Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 147 ◽  
Author(s):  
Muhammad Aslam ◽  
Jae-Myeong Lee ◽  
Hyung-Seung Kim ◽  
Seung-Jae Lee ◽  
Sugwon Hong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solar Radiation ◽  
Comparative Study ◽  
Short Term Memory ◽  
State Of The Art ◽  
Support Vector ◽  
Learning Approaches ◽  
Solar Generation

Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

scholarly journals VAECGAN: a generating framework for long-term prediction in multivariate time series

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xiang Yin ◽  
Yanni Han ◽  
Zhen Xu ◽  
Jie Liu
Keyword(s):  
Empirical Studies ◽  
Multivariate Time Series ◽  
Difficult Problem ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Term Prediction ◽  
Exogenous Sequence ◽  
Real World Datasets ◽  
Long Term Prediction

AbstractLong-term prediction is still a difficult problem in data mining. People usually use various kinds of methods of Recurrent Neural Network to predict. However, with the increase of the prediction step, the accuracy of prediction decreases rapidly. In order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN). Our model is divided into three parts. The first part is the encoder net, which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous sequence. The second part is the generator net which is responsible for generating prediction data. In the third part, the discriminator net is used to classify and feedback, adjust data generation and improve prediction accuracy. Finally, extensive empirical studies tested with five real-world datasets (NASDAQ, SML, Energy, EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach.

scholarly journals Long-term Prediction of Bridge Element Performance Using Time Delay Neural Networks (TDNNs)

2010 ◽  
Vol 97 (24) ◽  
pp. 1-7
Author(s):  
Jaeho Lee ◽  
Michael Blumenstein ◽  
Hong Guan ◽  
Yew-Chaye Loo
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Term Prediction ◽  
Long Term Prediction

Exploring the Long-Term Soil Moisture Predictability with FLUXNET Site Data using Circulating Learning Model

2021 ◽  
Author(s):  
Qingliang Li ◽  
Zhongyan Li ◽  
Wei Shangguan ◽  
Yifei Yao ◽  
Xuezhi Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Prediction Ability ◽  
Term Memory ◽  
Term Prediction ◽  
Long Short Term Memory ◽  
Delay Prediction

<p>The skillful long-term (from 3 days delay) prediction of soil moisture can provide more help than the short-term prediction of soil moisture for many practical applications including ecosystem management and precision agriculture. It presents great challenges because the far future variation of soil moisture has more uncertainties than the near future on soil moisture. Therefore, a novel circulating learning deep learning (DL) model based on Long Short-Term Memory (LSTM), is developed in this study as an alternative data-intelligence tool. This model includes two layers: the encoder-decoder LSTM layer and LSTM with a fully connected layer, which were used to enhance the long-term prediction ability by considering the intermediate time-series data between the input timestep and the predictive timestep. We applied this model by using FLUXNET2015 tie1 and tie2 subset data product over seven sites in different countries. The result shows that our model predicts soil moisture with better accuracy in average state and fluctuation pattern and amplitude when compared with other state-of-the-art DL methods, such as Multiple Linear Regression (MLR), Long Short-Term Memory (LSTM) and encoder-decoder LSTM models. Furthermore, the different-term (short-term, medium-term and long-term) predictability of soil moisture over various conditions (i.e., different hyper-parameters in our model, different predictive models, different climate regions and different sites) has been widely discussed in this paper. The code of our model is publicly available at https://github.com/ljz1228/CLM-LSTM-soil-moisture-prediction. We hope that this work will accelerate the research for long-term soil moisture prediction.</p>

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