Ensemble deep learning for regression and time series forecasting

Qiu, Xueheng; Zhang, Le; Ren, Yongmao; Suganthan, Ponnuthurai Nagaratnam; Amaratunga, G.A.J.

doi:10.1109/ciel.2014.7015739

Cited by 270 publications

(167 citation statements)

References 18 publications

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“…Little work has been done in the field of time series regression using DNNs, and almost no work has been done in the field of energy forecasting with DNNs. One notable example of literature on these subjects is Qui et al, who claim to be the first to use DNNs for regression and time series forecasting [13]. They show promising results on three electric load demand time series and several other time series using 20 DNNs ensembled with support vector regression.…”

Section: Prior Workmentioning

confidence: 99%

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

2018

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Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).

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Section: Prior Workmentioning

confidence: 99%

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

2018

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“…Deep-learning methods are machine learning algorithms [10] with multiple levels of representation are representation-learning methods, which are obtained by composing simple modules that each transforms the representation at one level into a representation at a higher, slightly more abstract level [11]. There are supervised learning algorithms namely recurrent network [12], convolutional neural network [13] and multilayer perceptron [14].…”

Section: Related Workmentioning

confidence: 99%

Construction Cost Prediction Using Neural Networks

Magdum

Adamuthe

2017

IJSC

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Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. 'elu' transfer function gives better results than other transfer function.

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“…In recent years, recurrent neural networks (RNN) have achieved remarkable success in a range of sequence modeling tasks (Lipton et al, 2015;Kuremoto et al, 2014;Qiu et al, 2014). Inspired by the success of recurrent neural networks with pre-trained word embeddings for text modeling, we use a stack of RNN layers for encoding the textual content of a post.…”

Section: Introductionmentioning

confidence: 99%

Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Halder¹,

Poddar²,

Kan³

2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual's posts. We analyze a realworld dataset from the Mental Health section of healthboards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely.We study the problem of predicting a patient's emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.

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Ensemble deep learning for regression and time series forecasting

Cited by 270 publications

References 18 publications

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Construction Cost Prediction Using Neural Networks

Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

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