Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models

The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously.

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Application of Deep Learning Methods to Forecasting Changes in Short-Term Currency Trends

Scientific Bulletin of Mukachevo State University Series “Economics” ◽

10.52566/msu-econ.7(2).2020.75-86 ◽

2020 ◽

Vol 7 (2) ◽

pp. 75-86

Author(s):

Vasily D. Derbentsev ◽

Vitalii S. Bezkorovainyi ◽

Iryna V. Luniak

Keyword(s):

Neural Networks ◽

Time Series ◽

Deep Learning ◽

Deep Neural Networks ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Better Than

This study investigates the issues of forecasting changes in short-term currency trends using deep learning models, which is relevant for both the scientific community and for traders and investors. The purpose of this study is to build a model for forecasting the direction of change in the prices of currency quotes based on deep neural networks. The developed architecture was based on the model of valve recurrent node, which is a modification of the model of “Long Short-Term Memory”, but is simpler in terms of the number of parameters and learning time. The forecast calculations of the dynamics of quotations of the currency pair euro/dollar and the most capitalised cryptocurrency Bitcoin/dollar were performed using daily, four-hour and hourly datasets. The obtained results of binary classification (forecast of the direction of trend change) when applying daily and hourly quotations turned out to be generally better than those of time series models or models of neural networks of other architecture (in particular, multilayer perceptron or “Long Short-Term Memory” models). According to the study results, the highest accuracy of classification was for the model of daily quotations for both euro/dollar – about 72%, and for Bitcoin/ dollar – about 69%. For four-hour and hourly time series, the accuracy of classification decreased, which can be explained both by the increase in the impact of “market noise” and the probable overfitting. Computer simulation has demonstrated that models predict a rising trend better than a declining one. The study confirmed the prospects for the application of deep learning models for short-term forecasting of time series of currency quotes. The use of the developed models proved to be effective for both fiat and cryptocurrencies. The proposed system of models based on deep neural networks can be used as a basis for developing an automated trading system in the foreign exchange market

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Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

10.1115/ipack2021-74066 ◽

2021 ◽

Author(s):

Pradeep Lall ◽

Tony Thomas ◽

Ken Blecker

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Remaining Useful Life ◽

Operating Conditions ◽

Short Term ◽

Term Memory ◽

Rectangular Pattern ◽

Useful Life ◽

Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

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Deep Learning with Long Short-Term Memory for Time Series Prediction

IEEE Communications Magazine ◽

10.1109/mcom.2019.1800155 ◽

2019 ◽

Vol 57 (6) ◽

pp. 114-119 ◽

Cited By ~ 43

Author(s):

Yuxiu Hua ◽

Zhifeng Zhao ◽

Rongpeng Li ◽

Xianfu Chen ◽

Zhiming Liu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽

10.3390/en13154017 ◽

2020 ◽

Vol 13 (15) ◽

pp. 4017 ◽

Cited By ~ 2

Author(s):

Dukhwan Yu ◽

Wonik Choi ◽

Myoungsoo Kim ◽

Ling Liu

Keyword(s):

Power Generation ◽

Deep Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Local Context ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory ◽

Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

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A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

Sensors ◽

10.3390/s20174887 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4887

Author(s):

Hailun Zhang ◽

Rui Fu

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Hybrid Approach ◽

Lateral Acceleration ◽

Short Term ◽

Lateral Velocity ◽

Term Memory ◽

Turning Behavior ◽

Long Short Term Memory

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

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CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419400050 ◽

2019 ◽

Vol 19 (01) ◽

pp. 1940005 ◽

Cited By ~ 3

Author(s):

ULAS BARAN BALOGLU ◽

ÖZAL YILDIRIM

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Algorithms ◽

Eeg Signals ◽

Short Term ◽

Term Memory ◽

Long Duration ◽

Proposed Model ◽

Clinical Scenarios ◽

Long Short Term Memory

Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6[Formula: see text]s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61[Formula: see text]Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.

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A deep learning framework for financial time series using stacked autoencoders and long-short term memory

PLoS ONE ◽

10.1371/journal.pone.0180944 ◽

2017 ◽

Vol 12 (7) ◽

pp. e0180944 ◽

Cited By ~ 189

Author(s):

Wei Bao ◽

Jun Yue ◽

Yulei Rao

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Financial Time Series ◽

Short Term ◽

Term Memory ◽

Learning Framework ◽

Financial Time ◽

Stacked Autoencoders ◽

Long Short Term Memory

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Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model

Applied Sciences ◽

10.3390/app11115141 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5141

Author(s):

Wenying Lyu ◽

Honghai Zhang ◽

Junqiang Wan ◽

Lei Yang

Keyword(s):

Time Series ◽

Traffic Safety ◽

Prediction Models ◽

Short Term Memory ◽

Model Performance ◽

Risk Identification ◽

Civil Aviation ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Traffic safety has been thought of as a basic feature of transportation, recent developments in civil aviation have emphasized the need for risk identification and safety prediction. This study aims to increase en-route flight safety through the development of prediction models for flight conflicts. Firstly, flight conflicts time series and traffic parameters are extracted from historical ADS-B data. In the second step, a Long Short-Term Memory (LSTM) model is trained to make a one-step-ahead prediction on the flight conflict time series. The results show that the LSTM model has the greatest prediction effect (MAE 0.3901) with comparison to other models. Based on that, we add traffic parameters (volume, density, velocity) into the LSTM model as new input variables and issue a comprehensive analysis of the relative predictive power of traffic parameters. The accuracy of prediction model is validated with a mean error of less than 3%. Based on the improvements of model performance brought by traffic parameters, LSTM models with a single traffic parameter are proposed for further discussion. The results illustrate that volume is the most important factor in promoting prediction accuracy and density has an advantage of improvement in the aspect of model stability.

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Development of a Model for Predicting the Direction of Daily Price Changes in the Forex Market Using Long Short-Term Memory

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.1.1015 ◽

2021 ◽

Vol 11 (1) ◽

pp. 61-67

Author(s):

Watthana Pongsena ◽

◽

Prakaidoy Sitsayabut ◽

Nittaya Kerdprasop ◽

Kittisak Kerdprasop ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Price Changes ◽

Term Memory ◽

The Future ◽

Long Short Term Memory

Forex is the largest global financial market in the world. Traditionally, fundamental and technical analysis are strategies that the Forex traders often used. Nowadays, advanced computational technology, Artificial Intelligence (AI) has played a significant role in the financial domain. Various applications based on AI technologies particularly machine learning and deep learning have been constantly developed. As the historical data of the Forex are time-series data where the values from the past affect the values that will appear in the future. Several existing works from other domains of applications have proved that the Long-Short Term Memory (LSTM), which is a particular kind of deep learning that can be applied to modeling time series, provides better performance than traditional machine learning algorithms. In this paper, we aim to develop a powerful predictive model targeting to predicts the daily price changes of the currency pairwise in the Forex market using LSTM. Besides, we also conduct an extensive experiment with the intention to demonstrate the effect of various factors contributing to the performance of the model. The experimental results show that the optimized LSTM model accurately predicts the direction of the future price up to 61.25 percent.

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Forecasting container throughput with long short-term memory networks

Industrial Management & Data Systems ◽

10.1108/imds-07-2019-0370 ◽

2019 ◽

Vol 120 (3) ◽

pp. 425-441 ◽

Cited By ~ 4

Author(s):

Sonali Shankar ◽

P. Vigneswara Ilavarasan ◽

Sushil Punia ◽

Surya Prakash Singh

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Moving Average ◽

Short Term ◽

Error Matrix ◽

Term Memory ◽

Content Type ◽

Forecasting Performance ◽

Long Short Term Memory

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

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