Forecasting exchange rate with deep belief networks

Chao, Jinhui; Shen, Furao; Zhao, Jinxi

doi:10.1109/ijcnn.2011.6033368

Cited by 39 publications

(28 citation statements)

References 14 publications

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“…This data set records 18 activities performed by 9 subjects wearing 3 IMUs and a HR-monitor. Each of data contains 54 columns per row and the columns contain the following data: timestamp (1), activityID (2), heart rate (3), IMU hand (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), IMU chest (21-37), and IMU ankle (38-54). In our experiment, we only select 7 activities which are "lying (1)," "sitting (2)," "standing (3)," "walking (4)," "running (5)," "cycling (6)," "Nordic walking (7)."…”

Section: 21mentioning

confidence: 99%

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A Cycle Deep Belief Network Model for Multivariate Time Series Classification

Wang

Hua

Hao

et al. 2017

Mathematical Problems in Engineering

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Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

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Section: 21mentioning

confidence: 99%

“…Taylor et al [14] proposed conditional RBM, an extension of the RBM, which is applied to human notion sequences. Chao et al [15] evaluated the DBN performance as a forecasting tool on predicting exchange rate. Längkvist et al [16] applied DBN for sleep stage classification and evaluated the performance.…”

Section: Introductionmentioning

confidence: 99%

A Cycle Deep Belief Network Model for Multivariate Time Series Classification

Wang

Hua

Hao

et al. 2017

Mathematical Problems in Engineering

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“…The use and importance of deep neural networks is increasing and very good results are achieved in images, audio and video pattern recognition (Larochelle et al, 2009;Ranzato et al, 2007;Arel et al, 2010;Chao et al, 2011). However, the main learning algorithms for this kind of neural network are unsupervised training algorithms, which use unlabelled data for their training.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, network traffic and time series, in general, are labelled data, requiring an unsupervised pre-training before the actual supervised training as a fine-tuning. Yet, as shown in Chao et al (2011), the DBN and restricted Boltzmann machine (RBM), which are deep learning methods, can be modified to work better with labelled data, i.e., time series datasets.…”

Section: Resultsmentioning

confidence: 99%

Computer network traffic prediction: a comparison between traditional and deep learning neural networks

Oliveira

Barbar

Soares

2016

IJBDI

115

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This paper compares four different artificial neural network approaches for computer network traffic forecast, such as: 1) multilayer perceptron (MLP) using the backpropagation as training algorithm; 2) MLP with resilient backpropagation (Rprop); (3) recurrent neural network (RNN); 4) deep learning stacked autoencoder (SAE). The computer network traffic is sampled from the traffic of the network devices that are connected to the internet. It is shown herein how a simpler neural network model, such as the RNN and MLP, can work even better than a more complex model, such as the SAE. Internet traffic prediction is an important task for many applications, such as adaptive applications, congestion control, admission control, anomaly detection and bandwidth allocation. In addition, efficient methods of resource management, such as the bandwidth, can be used to gain performance and reduce costs, improving the quality of service (QoS). The popularity of the newest deep learning methods have been increasing in several areas, but there is a lack of studies concerning time series prediction, such as internet traffic.

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“…Another popular prediction technique is the technical analysis prediction model also known as chartists, this model does not consider the company's financial fundamentals, but rather compute and predict the future price of a stock on past price trends this is known as time series analysis. (Chao, Shen, and Zhao (2011) purport that technical analyst's study patterns, statistical techniques and an example of such are the exponential moving average (EMA).…”

Section: Introductionmentioning

confidence: 99%