Optimization of LSTM Algorithm Through Outliers – Application to Financial Time Series Forecasting

Communications in Computer and Information Science - Smart Applications and Data Analysis ◽

10.1007/978-3-030-45183-7_16 ◽

2020 ◽

pp. 209-220

Author(s):

Houda Benkerroum ◽

Walid Cherif ◽

Mohamed Kissi

Keyword(s):

Time Series ◽

Financial Time Series ◽

Time Series Forecasting ◽

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Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data

Pattern Recognition Letters ◽

10.1016/j.patrec.2020.06.006 ◽

2020 ◽

Vol 136 ◽

pp. 183-189 ◽

Author(s):

Nikolaos Passalis ◽

Anastasios Tefas ◽

Juho Kanniainen ◽

Moncef Gabbouj ◽

Alexandros Iosifidis

Keyword(s):

Time Series ◽

Financial Time Series ◽

Limit Order Book ◽

Time Series Forecasting ◽

Limit Order ◽

Bag Of Features ◽

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Financial Time Series Forecasting with Multi-Modality Graph Neural Network

Pattern Recognition ◽

10.1016/j.patcog.2021.108218 ◽

2021 ◽

pp. 108218

Author(s):

Dawei Cheng ◽

Fangzhou Yang ◽

Sheng Xiang ◽

Jin Liu

Keyword(s):

Neural Network ◽

Time Series ◽

Financial Time Series ◽

Time Series Forecasting ◽

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Modified support vector machines in financial time series forecasting

Neurocomputing ◽

10.1016/s0925-2312(01)00676-2 ◽

2002 ◽

Vol 48 (1-4) ◽

pp. 847-861 ◽

Author(s):

Francis E.H. Tay ◽

L.J. Cao

Keyword(s):

Time Series ◽

Support Vector Machines ◽

Financial Time Series ◽

Time Series Forecasting ◽

Support Vector ◽

Financial Time ◽

Vector Machines

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Designing Translation Invariant Operators for Financial Time Series Forecasting

2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06) ◽

10.1109/sbrn.2006.15 ◽

2006 ◽

Author(s):

Ricardo de A. Araujo ◽

Robson P. de Sousa ◽

Tiago A. E. Ferreira

Keyword(s):

Time Series ◽

Financial Time Series ◽

Time Series Forecasting ◽

Translation Invariant ◽

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A Novel Hybrid Intelligent Model for Financial Time Series Forecasting and Its Application

2009 International Conference on Business Intelligence and Financial Engineering ◽

10.1109/bife.2009.71 ◽

2009 ◽

Author(s):

Wei Wang ◽

Hong Zhao ◽

Qiang Li ◽

Zhixiong Liu

Keyword(s):

Time Series ◽

Financial Time Series ◽

Time Series Forecasting ◽

Financial Time ◽

Intelligent Model

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Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting

Mining Data for Financial Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-37720-5_5 ◽

2020 ◽

pp. 60-69

Author(s):

Argimiro Arratia ◽

Eduardo Sepúlveda

Keyword(s):

Neural Networks ◽

Time Series ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Financial Time Series ◽

Time Series Forecasting ◽

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Financial Time Series Forecasting Using a Compound Model Based on Wavelet Frame and Support Vector Regression

2008 Fourth International Conference on Natural Computation ◽

10.1109/icnc.2008.455 ◽

2008 ◽

Author(s):

Wensheng Dai ◽

Chi-Jie Lu

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Financial Time Series ◽

Time Series Forecasting ◽

Support Vector ◽

Wavelet Frame ◽

Financial Time ◽

Model Based ◽

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Feature Selection for Support Vector Machines in Financial Time Series Forecasting

Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents - Lecture Notes in Computer Science ◽

10.1007/3-540-44491-2_38 ◽

2000 ◽

pp. 268-273 ◽

Author(s):

L. J. Cao ◽

Francis E. H. Tay

Keyword(s):

Time Series ◽

Feature Selection ◽

Support Vector Machines ◽

Financial Time Series ◽

Time Series Forecasting ◽

Support Vector ◽

Financial Time ◽

Vector Machines ◽

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Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting

Journal of Intelligent & Fuzzy Systems ◽

10.3233/ifs-130775 ◽

2014 ◽

Vol 26 (2) ◽

pp. 831-845 ◽

Author(s):

Mehdi Khashei ◽

Mehdi Bijari

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Financial Time Series ◽

Time Series Forecasting ◽

Financial Time ◽

Q Model ◽

Artificial Neural

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Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting

Applied Econometrics ◽

10.22394/1993-7601-2021-62-85-100 ◽

2021 ◽

Vol 62 ◽

pp. 85-100

Author(s):

Robert Garafutdinov ◽

Keyword(s):

Time Series ◽

Long Memory ◽

Financial Time Series ◽

Time Series Forecasting ◽

Model Parameters ◽

Memory Length ◽

Financial Time ◽

Arfima Model ◽

Practical Recommendations

The influence of ARFIMA model parameters on the accuracy of financial time series forecasting on the example of artificially generated long memory series and daily log returns of RTS index is investigated. The investigated parameters are deviation of the integration order value from its «true» value, as well as the memory «length» considered by the model. Based on the research results, some practical recommendations for modeling using ARFIMA have been formulated.

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