A comparison of SVR and NARX in financial time series forecasting

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Ayca Hatice Atli ◽  
Engin Tas
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time

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

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 ◽  
Order Book ◽  
Limit Order ◽  
Bag Of Features ◽  
Financial Time

Financial Time Series Forecasting with Multi-Modality Graph Neural Network

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 ◽  
Financial Time

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

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 ◽  
True Value ◽  
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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