scholarly journals Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Widi Hastomo ◽  
Adhitio Satyo Bayangkari Karno ◽  
Nawang Kalbuana ◽  
Ervina Nisfiani ◽  
Lussiana ETP
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer ◽  
Gated Recurrent Unit ◽  
Blue Chip

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

scholarly journals A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 348 ◽  
Author(s):  
Guang Yang ◽  
HwaMin Lee ◽  
Giyeol Lee
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Short Term Memory ◽  
Hybrid Models ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit ◽  
Concentration Levels

Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

scholarly journals The use of long short-term memory and gated recurrent unit for predicting the values of geomagnetic indices

2020 ◽  
pp. 110-121
Author(s):  
В.А. Мочалов ◽  
А.В. Мочалова
Keyword(s):  
Deep Learning ◽  
Initial Data ◽  
Short Term Memory ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Geomagnetic Indices ◽  
Source Data ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

В работе с помощью глубокого обучения рассматривается прогнозирование значений следующих геомагнитных индексов (ГИ): Dst, Kp, AE и AP. Для прогнозирования используются архитектуры долгой краткосрочной памяти (LSTM) и управляемых рекуррентных блоков (GRU). Для различных ГИ индексов анализируется функция потерь в за-висимости от периодичности исходных данных. Установлено, что чем меньше периодичность исходных данных ГИ тем точнее осуществляется прогноз следующего значения ГИ. Для анализа использовались следую-щие периоды исходных данных ГИ: час, 3 часа, сутки. In this work, with the help of deep learning, predicting the values of the following geomagnetic indices (GI) is considered: Dst, Kp, AE and Ap. For forecasting we use the architectures are long short-term memory (LSTM) and gated recurrent unit (GRU). For various GI indices, the loss function is analyzed depending on the periodicity of the source data. It has been established that forecasting accuracy increases with decreasing periodicity of the initial data of geomagnetic indices. For the analysis, the following periods of the initial GI data were used: hour, 3 hours, day. For the analysis we used hour, 3 hours and day periods of the initial GI source data.

scholarly journals Prediksi Harga Beras Menggunakan Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM)

2020 ◽  
Vol 12 (1) ◽  
pp. 35-41
Author(s):  
Steven Sen ◽  
Dedy Sugiarto ◽  
Abdul Rochman
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer

Beras adalah salah satu komoditas utama di masyarakat Indonesia. Masalah utama dengan beras secara nasional adalah inflasi harga beras. Oleh karena itu, penelitian ini memprediksi harga beras menggunakan arsitektur jaringan saraf tiruan Multilayer Perceptron (MLP) dan deep learning : Long Short Term Memory (LSTM) untuk mengantisipasi masalah ini. Data yang digunakan dalam penelitian ini adalah data riil harga beras selama 2016 - 2019 yang diperoleh dari PT. Food Station. Total dataset adalah 1307 dengan distribusi 1123 sebagai data train dan 184 sebagai data uji. Hasil akhir yang diperoleh dalam penelitian ini adalah LSTM lebih unggul dari MLP, dengan nilai Root Mean Square Error (RMSE) data train : 0,49, dan nilai loss RMSE dari data tes adalah 0,27. Model LSTM paling optimal dari 3 tes dilakukan, yaitu jumlah hidden layer = 16 dan epochs = 150 kali.

scholarly journals An LSTM and GRU based Trading Strategy Adapted to the Moroccan Market

2021 ◽  
Author(s):  
Yassine Touzani ◽  
Khadija Douzi
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Decision Rules ◽  
Trading Strategy ◽  
Local Market ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

Abstract Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). Predicting the closing price provides useful information and helps the investor to make the right decision. The use of deep learning and more precisely the recurrent neural networks RNNs in stock market forecasting is an increasingly common practice in the literature. The Long Short Term Memory LSTM and Gated Recurrent Unit GRU architectures are among the most widely used types of RNN networks, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the Moroccan stock market, based on two deep learning model: Long Short Term Memory LSTM and Gated Recurrent Unit GRU to predict respectively close price for short and mid term horizon. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four three-years periods, we simulate transactions using these decision rules with different parameters for each stock. We only hold stocks that ensure a return greater than a benchmark return over the four periods. Random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. The repetition of this process with a variation of benchmark return makes it possible to select the best possible combination of stock each with the parameters optimized for the decision rules. The proposed strategy produces very promising results and outperform the performance of indices used as benchmarks in the local market.

scholarly journals A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4884
Author(s):  
Danish Javeed ◽  
Tianhan Gao ◽  
Muhammad Taimoor Khan ◽  
Ijaz Ahmad
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

scholarly journals Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden

2021 ◽  
Author(s):  
Claire Brenner ◽  
Jonathan Frame ◽  
Grey Nearing ◽  
Karsten Schulz
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Sich Eine ◽  
Short Term ◽  
Term Memory ◽  
Neuronales Netzwerk ◽  
Long Short Term Memory

ZusammenfassungDie Verdunstung ist ein entscheidender Prozess im globalen Wasser‑, Energie- sowie Kohlenstoffkreislauf. Daten zur räumlich-zeitlichen Dynamik der Verdunstung sind daher von großer Bedeutung für Klimamodellierungen, zur Abschätzung der Auswirkungen der Klimakrise sowie nicht zuletzt für die Landwirtschaft.In dieser Arbeit wenden wir zwei Machine- und Deep Learning-Methoden für die Vorhersage der Verdunstung mit täglicher und halbstündlicher Auflösung für Standorte des FLUXNET-Datensatzes an. Das Long Short-Term Memory Netzwerk ist ein rekurrentes neuronales Netzwerk, welchen explizit Speichereffekte berücksichtigt und Zeitreihen der Eingangsgrößen analysiert (entsprechend physikalisch-basierten Wasserbilanzmodellen). Dem gegenüber gestellt werden Modellierungen mit XGBoost, einer Entscheidungsbaum-Methode, die in diesem Fall nur Informationen für den zu bestimmenden Zeitschritt erhält (entsprechend physikalisch-basierten Energiebilanzmodellen). Durch diesen Vergleich der beiden Modellansätze soll untersucht werden, inwieweit sich durch die Berücksichtigung von Speichereffekten Vorteile für die Modellierung ergeben.Die Analysen zeigen, dass beide Modellansätze gute Ergebnisse erzielen und im Vergleich zu einem ausgewerteten Referenzdatensatz eine höhere Modellgüte aufweisen. Vergleicht man beide Modelle, weist das LSTM im Mittel über alle 153 untersuchten Standorte eine bessere Übereinstimmung mit den Beobachtungen auf. Allerdings zeigt sich eine Abhängigkeit der Güte der Verdunstungsvorhersage von der Vegetationsklasse des Standorts; vor allem wärmere, trockene Standorte mit kurzer Vegetation werden durch das LSTM besser repräsentiert, wohingegen beispielsweise in Feuchtgebieten XGBoost eine bessere Übereinstimmung mit den Beobachtung liefert. Die Relevanz von Speichereffekten scheint daher zwischen Ökosystemen und Standorten zu variieren.Die präsentierten Ergebnisse unterstreichen das Potenzial von Methoden der künstlichen Intelligenz für die Beschreibung der Verdunstung.

scholarly journals Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1953
Author(s):  
Seyed Majid Azimi ◽  
Maximilian Kraus ◽  
Reza Bahmanyar ◽  
Peter Reinartz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Object Tracking ◽  
Short Term Memory ◽  
Aerial Imagery ◽  
Future Research ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

Sign in / Sign up

Export Citation Format

Share Document