Swarm-based translation-invariant morphological prediction method for financial time series forecasting

2010 ◽  
Vol 180 (24) ◽  
pp. 4784-4805 ◽  
Author(s):  
Ricardo de A. Araújo
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Prediction Method ◽  
Time Series Forecasting ◽  
Translation Invariant ◽  
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

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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