Stock Volume Prediction Based on Polarity of Tweets, News, and Historical Data Using Deep Learning

2020 ◽  
Author(s):  
Navaneeth Jawahar ◽  
Jeyaprakash Chelladurai ◽  
Imayabharathi Sakthivel ◽  
Biju Bajracharya
Keyword(s):  
Deep Learning ◽  
Historical Data ◽  
Stock Volume

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals The Use of Remotely Sensed Data and Polish NFI Plots for Prediction of Growing Stock Volume Using Different Predictive Methods

2020 ◽  
Vol 12 (20) ◽  
pp. 3331
Author(s):  
Paweł Hawryło ◽  
Saverio Francini ◽  
Gherardo Chirici ◽  
Francesca Giannetti ◽  
Karolina Parkitna ◽  
...  
Keyword(s):  
Deep Learning ◽  
Remotely Sensed ◽  
Parameter Estimates ◽  
Remotely Sensed Data ◽  
Positional Accuracy ◽  
Growing Stock ◽  
3D Point Clouds ◽  
Predictive Methods ◽  
Predictive Approaches ◽  
Stock Volume

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.

scholarly journals An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Zeynep Hilal Kilimci ◽  
A. Okay Akyuz ◽  
Mitat Uysal ◽  
Selim Akyokus ◽  
M. Ozan Uysal ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Support Vector Regression ◽  
Historical Data ◽  
Demand Forecasting ◽  
Support Vector ◽  
Series Analysis ◽  
Integration Strategy ◽  
Forecasting System

Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.

scholarly journals Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

2019 ◽  
Vol 33 ◽  
pp. 782-790 ◽  
Author(s):  
Mike Wu ◽  
Milan Mosse ◽  
Noah Goodman ◽  
Chris Piech
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Historical Data ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Human In The Loop ◽  
Modern Computer ◽  
Teacher Effort ◽  
Education Platform ◽  
New Algorithms

In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop “rubric sampling” approach to tackle the “zero shot” feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student’s solution and can articulate a student’s misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world’s largest programming education platform.

scholarly journals Continuous Training and Deployment of Deep Learning Models

2021 ◽  
Author(s):  
Ioannis Prapas ◽  
Behrouz Derakhshan ◽  
Alireza Rezaei Mahdiraji ◽  
Volker Markl
Keyword(s):  
Deep Learning ◽  
Historical Data ◽  
State Of The Art ◽  
Streaming Data ◽  
Superior Performance ◽  
Learning Models ◽  
Model Quality ◽  
Continuous Training ◽  
Training Time ◽  
Machine Learning Methods

AbstractDeep Learning (DL) has consistently surpassed other Machine Learning methods and achieved state-of-the-art performance in multiple cases. Several modern applications like financial and recommender systems require models that are constantly updated with fresh data. The prominent approach for keeping a DL model fresh is to trigger full retraining from scratch when enough new data are available. However, retraining large and complex DL models is time-consuming and compute-intensive. This makes full retraining costly, wasteful, and slow. In this paper, we present an approach to continuously train and deploy DL models. First, we enable continuous training through proactive training that combines samples of historical data with new streaming data. Second, we enable continuous deployment through gradient sparsification that allows us to send a small percentage of the model updates per training iteration. Our experimental results with LeNet5 on MNIST and modern DL models on CIFAR-10 show that proactive training keeps models fresh with comparable—if not superior—performance to full retraining at a fraction of the time. Combined with gradient sparsification, sparse proactive training enables very fast updates of a deployed model with arbitrarily large sparsity, reducing communication per iteration up to four orders of magnitude, with minimal—if any—losses in model quality. Sparse training, however, comes at a price; it incurs overhead on the training that depends on the size of the model and increases the training time by factors ranging from 1.25 to 3 in our experiments. Arguably, a small price to pay for successfully enabling the continuous training and deployment of large DL models.

scholarly journals Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1830
Author(s):  
Banalaxmi Brahma ◽  
Rajesh Wadhvani
Keyword(s):  
Deep Learning ◽  
Solar Irradiance ◽  
Goodness Of Fit ◽  
Historical Data ◽  
Green Energy ◽  
Multiple Site ◽  
Forecast Performance ◽  
Single Location ◽  
Rolling Window ◽  
Irradiance Data

The ever-growing interest in and requirement for green energy have led to an increased focus on research related to forecasting solar irradiance recently. This study aims to develop forecast models based on deep learning (DL) methodologies and multiple-site data to predict the daily solar irradiance in two locations of India based on the daily solar radiation data obtained from NASA’s POWER project repository over 36 years (1983–2019). The forecast modeling of solar irradiance data is performed for extracting and learning the symmetry latent in data patterns and relationships by the machine learning models and utilizing it to predict future solar data. The goodness of fit and model performance are compared with rolling window evaluation using mean squared error, root-mean-square error and coefficient of determination (R2) for evaluation. The contributions of this study can be summarized as follows: (i) time series models based on deep learning methodologies were implemented to forecast the daily solar irradiance of two locations in India in consideration of the historical data collected by NASA; (ii) the models were developed on the basis of single-location univariate data as well as multiple-location data; (iii) the accuracy, performance and reliability of the model were investigated on the basis of standard performance evaluation metrics and rolling window evaluation; (iv) the feature importance of the nearby locations with respect to forecasting target location solar irradiance was analyzed and compared based on the solar irradiance data obtained from NASA over 36 years. The results indicate that the bidirectional long short-term memory (LSTM) and attention-based LSTM models can be used for forecasting daily solar irradiance data. According to the findings, the multiple-site data with solar irradiance historical data improve upon the forecast performance of single-location univariate solar data.

scholarly journals Use of Appropriate Loss Function in Rainfall Prediction using Deep Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 293-297
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Loss Function ◽  
Historical Data ◽  
Daily Rainfall ◽  
Cross Entropy ◽  
Learning Approach ◽  
Rainfall Prediction

India is an agricultural country, and rainfall is the main source of irrigation for agriculture. Prediction of rainfall is very crucial for farmers to make decisions. In this research paper, the prediction model has been developed through deep learning using historical data of 10 years of rainfall. A deep learning approach used Keras API with an artificial neural network technique to predict the daily rainfall. The prediction model has been assessed by four-loss function, i.e., MSE, MAE, Hinge, and Binary Cross-Entropy.

scholarly journals Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 357
Author(s):  
Dabeeruddin Syed ◽  
Shady S. Refaat ◽  
Othmane Bouhali
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Smart Grids ◽  
Historical Data ◽  
Homomorphic Encryption ◽  
Load Forecasting ◽  
Security And Privacy ◽  
Learning Models ◽  
Privacy And Security

Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.

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