Financial Time Series Prediction Using Spiking Neural Networks
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{"title"=>"Financial time series prediction using spiking neural networks", "type"=>"journal", "authors"=>[{"first_name"=>"David", "last_name"=>"Reid", "scopus_author_id"=>"19934745600"}, {"first_name"=>"Abir Jaafar", "last_name"=>"Hussain", "scopus_author_id"=>"56212648400"}, {"first_name"=>"Hissam", "last_name"=>"Tawfik", "scopus_author_id"=>"10039993800"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"373867591", "sgr"=>"84929504453", "issn"=>"19326203", "pmid"=>"25170618", "scopus"=>"2-s2.0-84929504453", "doi"=>"10.1371/journal.pone.0103656", "isbn"=>"0018726708094"}, "id"=>"b6387e81-a5dc-3bea-8094-ccfcae74621f", "abstract"=>"In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two \"traditional\", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.", "link"=>"http://www.mendeley.com/research/financial-time-series-prediction-using-spiking-neural-networks-1", "reader_count"=>50, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Student > Doctoral Student"=>4, "Researcher"=>5, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>5, "Other"=>5, "Student > Master"=>4, "Student > Bachelor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Student > Doctoral Student"=>4, "Researcher"=>5, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>5, "Other"=>5, "Student > Master"=>4, "Student > Bachelor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>7, "Mathematics"=>5, "Neuroscience"=>1, "Business, Management and Accounting"=>2, "Physics and Astronomy"=>1, "Psychology"=>1, "Computer Science"=>21, "Economics, Econometrics and Finance"=>8}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>7}, "Neuroscience"=>{"Neuroscience"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>8}, "Computer Science"=>{"Computer Science"=>21}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Turkey"=>1, "Iran"=>1, "Switzerland"=>1, "India"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1654943"], "description"=>"<p>Brent crude oil prices (5-step prediction).</p>", "links"=>[], "tags"=>["novel application", "Maximum Drawdown", "Spiking Neural Networks", "Linear Predictor Coefficients model", "IBM stock data", "time series", "spiking neural network", "Polychronous Spiking Network", "Brent crude oil", "prediction error", "Annualised Return", "Dynamic Ridge Polynomial", "data forecasting", "Financial Time Series Prediction", "favourable prediction results", "time series prediction"], "article_id"=>1155521, "categories"=>["Biological Sciences"], "users"=>["David Reid", "Abir Jaafar Hussain", "Hissam Tawfik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103656.g010", "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brent_crude_oil_prices_5_step_prediction_/1155521", "title"=>"Brent crude oil prices (5-step prediction).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-29 02:43:33"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1654945"], "description"=>"<p>Time series data used in the experiments.</p>", "links"=>[], "tags"=>["novel application", "Maximum Drawdown", "Spiking Neural Networks", "Linear Predictor Coefficients model", "IBM stock data", "time series", "spiking neural network", "Polychronous Spiking Network", "Brent crude oil", "prediction error", "Annualised Return", "Dynamic Ridge Polynomial", "data forecasting", "Financial Time Series Prediction", "favourable prediction results", "time series prediction"], "article_id"=>1155523, "categories"=>["Biological Sciences"], "users"=>["David Reid", "Abir Jaafar Hussain", "Hissam Tawfik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103656.t001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_series_data_used_in_the_experiments_/1155523", "title"=>"Time series data used in the experiments.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-29 02:43:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/1654946"], "description"=>"<p>n is the total number of data patterns.</p><p><i>y</i> and represent the actual and predicted output value.</p><p>Signal processing and trading simulation performance measures.</p>", "links"=>[], "tags"=>["novel application", "Maximum Drawdown", "Spiking Neural Networks", "Linear Predictor Coefficients model", "IBM stock data", "time series", "spiking neural network", "Polychronous Spiking Network", "Brent crude oil", "prediction error", "Annualised Return", "Dynamic Ridge Polynomial", "data forecasting", "Financial Time Series Prediction", "favourable prediction results", "time series prediction"], "article_id"=>1155524, "categories"=>["Biological Sciences"], "users"=>["David Reid", "Abir Jaafar Hussain", "Hissam Tawfik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103656.t002", "stats"=>{"downloads"=>5, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Signal_processing_and_trading_simulation_performance_measures_/1155524", "title"=>"Signal processing and trading simulation performance measures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-29 02:43:33"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1654949"], "description"=>"<p>1,5,10 and 15 step ahead prediction Signal to Noise Ratio for the Linear Predictor Classifier.</p>", "links"=>[], "tags"=>["novel application", "Maximum Drawdown", "Spiking Neural Networks", "Linear Predictor Coefficients model", "IBM stock data", "time series", "spiking neural network", "Polychronous Spiking Network", "Brent crude oil", "prediction error", "Annualised Return", "Dynamic Ridge Polynomial", "data forecasting", "Financial Time Series Prediction", "favourable prediction results", "time series prediction"], "article_id"=>1155527, "categories"=>["Biological Sciences"], "users"=>["David Reid", "Abir Jaafar Hussain", "Hissam Tawfik"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0103656.t004", "stats"=>{"downloads"=>5, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_1_5_10_and_15_step_ahead_prediction_Signal_to_Noise_Ratio_for_the_Linear_Predictor_Classifier_/1155527", "title"=>"1,5,10 and 15 step ahead prediction Signal to Noise Ratio for the Linear Predictor Classifier.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-29 02:43:33"}
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Relative Metric

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