scholarly journals Analysis of Lumber Prices Time Series Using Long Short-Term Memory Artificial Neural Networks

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 428
Author(s):  
Dercilio Junior Verly Lopes ◽  
Gabrielly dos Santos Bobadilha ◽  
Amanda Peres Vieira Bedette
Keyword(s):  
Stock Prices ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Short Term ◽  
Term Memory ◽  
Squared Error ◽  
Dense Output ◽  
The Usa ◽  
Long Short Term Memory

This manuscript confirms the feasibility of using a long short-term memory (LSTM) recurrent neural network (RNN) to forecast lumber stock prices during the great and Coronavirus disease 2019 (COVID-19) pandemic recessions in the USA. The database was composed of 5012 data entries divided into recession periods. We applied a timeseries cross-validation that divided the dataset into an 80:20 training/validation ratio. The network contained five LSTM layers with 50 units each followed by a dense output layer. We evaluated the performance of the network via mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) for 30, 60, and 120 timesteps and the recession periods. The metrics results indicated that the network was able to capture the trend for both recession periods with a remarkably low degree of error. Timeseries forecasting may help the forest and forest product industries to manage their inventory, transportation costs, and response readiness to critical economic events.

Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks

2021 ◽  
pp. 036119812110007
Author(s):  
Seifeldeen Eteifa ◽  
Hesham A. Rakha ◽  
Hoda Eldardiry
Keyword(s):  
Loss Function ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Loss Functions ◽  
Short Term ◽  
Mean Relative Error ◽  
Squared Error ◽  
The Mean ◽  
Long Short Term Memory

Vehicle acceleration and deceleration maneuvers at traffic signals result in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This study details a four-step long short-term memory (LSTM) deep learning based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models includes controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function that is proposed in this paper. The results show that while the proposed loss function outperforms conventional loss functions in overall absolute error values, the choice of the loss function is dependent on the prediction horizon. Specifically, the proposed loss function is slightly outperformed by the mean relative error for very short prediction horizons (less than 20 s) and the mean squared error for very long prediction horizons (greater than 120 s).

scholarly journals A Three-Stage Data-Driven Approach for Determining Reaction Wheels’ Remaining Useful Life Using Long Short-Term Memory

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2432
Author(s):  
Md Sirajul Islam ◽  
Afshin Rahimi
Keyword(s):  
System Parameter ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Remaining Useful Life ◽  
Health Index ◽  
Short Term ◽  
Term Memory ◽  
Squared Error ◽  
Useful Life ◽  
Long Short Term Memory

Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable system parameter of a faulty reaction wheel induced with incipient fault to estimate the remaining useful life of the reaction wheels. We achieve this goal in three stages, as none of the observable system parameters are directly related to the health of a reaction wheel. In the first stage, we identify the necessary observable system parameter and predict the future of these parameters using sensor acquired data and a long short-term memory recurrent neural network. In the second stage, we estimate the health index parameter using a multivariate long short-term memory network. In the third stage, we predict the remaining useful life of reaction wheels based on historical data of the health index parameter. Normalized root mean squared error is used to evaluate the performance of the various models in each stage. Additionally, three different timespans (short, moderate, and extended in the scale of small satellite orbit times) are simulated and tested for the performance of the proposed methodology regarding the malfunction of reaction wheels. Furthermore, the robustness of the proposed method to missing values, input frequency, and noise is studied. The results show promising performance for the proposed scheme with accuracy in predicting health index parameter around 0.01–0.02 normalized root mean squared error, the accuracy in prediction of RUL of 1%–2.5%, and robustness to various uncertainty factors, as discussed above.

scholarly journals PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Marie Luthfi Ashari ◽  
Mujiono Sadikin
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Long Short Term Memory

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan data training dan data testing. Di mana hasil menunjukan bahwa Regresi LSTM memiliki nilai prediksi penjualan dengan evaluasi model melalui RMSE sebesar 286.465.424 untuk data training dan 187.013.430 untuk data testing. Untuk nilai MAPE sebesar 787% dan 309% untuk data training dan data testing secara berurut.

scholarly journals Application of Long Short Term Memory to Predict Flight Delay on Commercial Flights

2020 ◽  
Vol 4 (3) ◽  
pp. 447-453
Author(s):  
Muhammad Genta Ari Shandi ◽  
Rifki Adhitama ◽  
Amalia Beladinna Arifa
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Short Term ◽  
Term Memory ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Long Short Term Memory ◽  
Flight Delay ◽  
Unpleasant Experience ◽  
Better Than

Delay in airline services, become an unpleasant experience for passengers who experience it. This study aims to build a model that can predict flight delay (departure) using the Long Short Term Memory method and can find out its performance. In this study there are two scenarios that have different ways of preprocessing. Both of these scenarios produce predictions with error values calculated using Root Mean Squared Error (RMSE), respectively from the first to the second scenario namely: 41, 21. Between the two, the second scenario is better than the first scenario due to extreme data deletion ( anomaly) in the second scenario with an error value using RMSE of 0.116.

scholarly journals PREDIKSI HARGA FOREIGN EXCHANGE MATA UANG EUR/USD DAN GBP/USD MENGGUNAKAN LONG SHORT-TERM MEMORY

2021 ◽  
Vol 2 (1) ◽  
pp. 16-31
Author(s):  
Angel Joanna Wijaya ◽  
Windra Swastika ◽  
Oesman Hendra Kelana
Keyword(s):  
Foreign Exchange ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Short Term ◽  
Term Memory ◽  
Squared Error ◽  
Long Short Term Memory

Foreign exchange (Forex) adalah perdagangan pasangan mata uang dari harga mata uang suatu negara terhadap mata uang negara lainnya. Pada penelitian ini menggunakan metode Long Short-Term Memory (LSTM) untuk memprediksi harga close mata uang EUR/USD (Euro terhadap Dolar Amerika) dan GBP/USD (Pound Sterling terhadap Dolar Amerika) pada candle D1 (1 hari) dengan input harga open dan close. Hasil yang diperoleh model EUR/USD dengan 1 input mendapatkan nilai Mean Squared Error (MSE) terendah yaitu 0,0535 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada model 3 input mendapatkan nilai MSE terendah yaitu 0,0529 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Sedangkan, model 5 input mendapatkan nilai MSE terendah yaitu 0,0469 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada mata uang GBP/USD, model dengan 1 input mendapatkan nilai MSE terendah yaitu 0,0543 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Nadam. Pada model 3 input mendapatkan nilai MSE terendah yaitu 0,0520 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Adam. Sedangkan, model 5 input mendapatkan nilai MSE terendah yaitu 0,0631 dengan model 1 layer LSTM 10 node dan menggunakan optimizer Adam. Model dengan MSE terbaik yang digunakan pada website yang dibuat. Rekomendasi yang diberikan adalah pada mata uang EUR/USD menggunakan model 5 input dan mata uang GBP/USD menggunakan model 3 input. Hasil loss MSE, akurasi MSE, dan jumlah profit yang didapatkan semuanya adalah yang terbaik jika dibandingkan dengan model dengan jumlah input lainnya

scholarly journals Featureless Blood Pressure Estimation Based on Photoplethysmography Signal Using CNN and BiLSTM for IoT Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yung-Hui Li ◽  
Latifa Nabila Harfiya ◽  
Ching-Chun Chang
Keyword(s):  
Blood Pressure ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Feature Engineering ◽  
Short Term ◽  
Squared Error ◽  
Long Short Term Memory ◽  
Iot Devices ◽  
Blood Pressure Estimation

Continuous blood pressure (BP) acquisition is critical to health monitoring of an individual. Photoplethysmography (PPG) is one of the most popular technologies in the last decade used for measuring blood pressure noninvasively. Several approaches have been carried out in various ways to utilize features extracted from PPG. In this study, we develop a continuous systolic and diastolic blood pressure (SBP and DBP) estimation mechanism without the need for any feature engineering. The raw PPG signal only got preprocessed before being fed to our model which mainly consists of one-dimensional convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) network. We evaluate the resulting SBP and DBP value by the root-mean-squared error (RMSE) and mean absolute error (MAE). This study addresses the effectiveness of the model by outperforming the previous feature engineering-based methods. We achieve RMSE of 11.503 and 6.525 for SBP and DBP, respectively, and MAE of 7.849 and 4.418 for SBP and DBP, respectively. The proposed method is expected to substantially enhance the current efficiency of healthcare IoT (Internet of Things) devices in BP monitoring using PPG signals only.

scholarly journals A Stock Closing Price Prediction Model Based on CNN-BiSLSTM

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haiyao Wang ◽  
Jianxuan Wang ◽  
Lihui Cao ◽  
Yifan Li ◽  
Qiuhong Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Short Term ◽  
Term Memory ◽  
Closing Price ◽  
Long Short Term Memory

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error (MAE), root-mean-squared error (RMSE), and R-square (R2) evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

scholarly journals Identification of Gender based on Blogs using Attention-based Recurrent Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 3152-3158
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Absolute Error ◽  
Digital Content ◽  
Human Beings ◽  
Short Term ◽  
Neural Network Models ◽  
Term Memory ◽  
Long Short Term Memory

With the digitization, the importance of content writing is being increased. This is due to the huge improvement in accessibility and the major impact of digital content on human beings. Due to veracity and huge demand for digital content, author profiling becomes a necessity to identify the correct person for particular content writing. This paper works on deep neural network models to identify the gender of author for any particular content. The analysis has been done on the corpus dataset by using artificial neural networks with different number of layers, long short term memory based Recurrent Neural Network (RNN), bidirectional long short term memory based RNN and attention-based RNN models using mean absolute error, root mean square error, accuracy, and loss as analysis parameters. The results of different epochs show the significance of each model.

scholarly journals Stock Prediction Using Sentiment Analysis and LSTM

2021 ◽  
Vol 9 (9) ◽  
pp. 1752-1756
Author(s):  
Ms. Anjima K. S
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Programming Language ◽  
Stock Prices ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Python Programming Language ◽  
Long Short Term Memory ◽  
Python Programming

Abstract: The stock market is a difficult area to anticipate since it is influenced by a variety of variables at the same time. The stock exchange is where equities are exchanged, transferred, and circulated. This research proposes a hybrid algorithm that predicts a stock's next day closing prices using sentiment analysis and Long Short Term Memory. The LSTM model seems to be quite popular in time-series forecasting, which is why it was selected for this project. Our proposed methodology makes use of the temporal association between public opinion and stock prices. Part-of-speech tagging is used to do sentiment analysis, and Long Short Term Memory is utilized to predict the stock's next day closing price. When these two factors are combined, we get a good picture of the stock's future. In this project, two main datasets have been used: HCLTECH company stock data and the news related to each stock of the HCL company for each day. The project is implemented by using the python programming language. The python programming language has been used to execute the project. This also incorporates machine learning along with public feedback. Sentiment analysis enables us to evaluate a diversity of political and economic factors, which have a significant impact on the stock market. Keywords: LSTM, sentiment analysis, RNN, Back propagation neural network.

Sign in / Sign up

Export Citation Format

Share Document