Deep Learning Model for Financial Time Series Prediction

2020 ◽  
Author(s):  
Omnia Kelany ◽  
Sherin Aly ◽  
Mohamed A. Ismail
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Learning Model ◽  
Financial Time ◽  
Deep Learning Model

scholarly journals An Interpretable Deep Learning Model for Early Prediction of Sepsis in the Emergency Department

2020 ◽  
Author(s):  
Dongdong Zhang ◽  
Changchang Yin ◽  
Katherine M. Hunold ◽  
Xiaoqian Jiang ◽  
Jeffrey M. Caterino ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Science ◽  
Time Series Prediction ◽  
Learning Model ◽  
Attention Mechanism ◽  
Early Prediction ◽  
Max Pooling ◽  
Time Encoding ◽  
Deep Learning Model

Background: Sepsis, a life-threatening illness caused by the body's response to an infection, is the leading cause of death worldwide and has become a global epidemiological burden. Early prediction of sepsis increases the likelihood of survival for septic patients. Methods The 2019 DII National Data Science Challenge enabled participating teams to develop models for early prediction of sepsis onset with de-identified electronic health records of over 100,000 unique patients. Our task is to predict sepsis onset 4 hours before its diagnosis using basic administrative and demographics, time-series vital, lab, nutrition as features. An LSTM-based model with event embedding and time encoding is proposed to model time-series prediction. We utilized the attention mechanism and global max pooling techniques to enable interpretation for the proposed deep learning model. Results We evaluated the performance of the proposed model on 2 use cases of sepsis onset prediction which achieved AUC scores of 0.940 and 0.845, respectively. Our team, BuckeyeAI achieved an average AUC of 0.892 and the official rank is #2 out of 30 participants. Conclusions Our model outperformed collapsed models (i.e., logistic regression, random forest, and LightGBM). The proposed LSTM-based model handles irregular time intervals by incorporating time encoding and is interpretable thanks to the attention mechanism and global max pooling techniques.

scholarly journals Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 575
Author(s):  
Zhenghe Li ◽  
Ling Kang ◽  
Liwei Zhou ◽  
Modi Zhu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Input Data ◽  
Time Series Prediction ◽  
Learning Model ◽  
Series Analysis ◽  
Analysis Methods ◽  
Runoff Prediction ◽  
Deep Learning Model

Recent advances in deep learning, especially the long short-term memory (LSTM) networks, provide some useful insights on how to tackle time series prediction problems, not to mention the development of a time series model itself for prediction. Runoff forecasting is a time series prediction problem with a series of past runoff data (water level and discharge series data) as inputs and a fixed-length series of future runoff as output. Most previous work paid attention to the sufficiency of input data and the structural complexity of deep learning, while less effort has been put into the consideration of data quantity or the processing of original input data—such as time series decomposition, which can better capture the trend of runoff—or unleashing the effective potential of deep learning. Mutual information and seasonal trend decomposition are two useful time series methods in handling data quantity analysis and original data processing. Based on a former study, we proposed a deep learning model combined with time series analysis methods for daily runoff prediction in the middle Yangtze River and analyzed its feasibility and usability with frequently used counterpart models. Furthermore, this research also explored the data quality that affect the performance of the deep learning model. With the application of the time series method, we can effectively get some information about the data quality and data amount that we adopted in the deep learning model. The comparison experiment resulted in two different sites, implying that the proposed model improved the precision of runoff prediction and is much easier and more effective for practical application. In short, time series analysis methods can exert great potential of deep learning in daily runoff prediction and may unleash great potential of artificial intelligence in hydrology research.

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

scholarly journals Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks

2020 ◽  
Vol 12 (6) ◽  
pp. 21-32
Author(s):  
Muhammad Zulqarnain ◽  
◽  
Rozaida Ghazali ◽  
Muhammad Ghulam Ghouse ◽  
Yana Mazwin Mohmad Hassim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time ◽  
Data Mining Approach ◽  
Learning Tasks ◽  
Financial Prices ◽  
Stock Indexes

Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.

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