Remarks on the possible universal mechanism of the non-linear long-term autocorrelations in financial time-series

2004 ◽  
Vol 344 (1-2) ◽  
pp. 244-251 ◽  
Author(s):  
Ryszard Kutner ◽  
Filip Świtała
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time ◽  
Non Linear ◽  
Universal Mechanism

scholarly journals Forecasting Financial Budget Time Series: ARIMA Random Walk vs LSTM Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 317
Author(s):  
Maryem Rhanoui ◽  
Siham Yousfi ◽  
Mounia Mikram ◽  
Hajar Merizak
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Financial Time Series ◽  
Arima Model ◽  
Economic Factors ◽  
Optimal Management ◽  
Financial Time ◽  
Management Of Resources ◽  
Non Linear ◽  
Predictive Approaches

<div style="’text-align: justify;"><p>Financial time series are volatile, non-stationary and non-linear data that are affected by external economic factors. There is several performant predictive approaches such as univariate ARIMA model and more recently Recurrent Neural Network. The accurate forecasting of budget data is a strategic and challenging task for an optimal management of resources, it requires the use of the most accurate model. We propose a predictive approach that uses and compares the Machine Learning ARIMA model and Deep Learning Recurrent LSTM model. The application and the comparative analysis shows that the LSTM model outperforms the ARIMA model, mainly thanks to the LSTMs ability to learn non-linear relationship from data.</p></div>

scholarly journals Forecasting economic and financial time-series with non-linear models

2004 ◽  
Vol 20 (2) ◽  
pp. 169-183 ◽  
Author(s):  
Michael P. Clements ◽  
Philip Hans Franses ◽  
Norman R. Swanson
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Financial Time Series ◽  
Financial Time ◽  
Non Linear
Sign in / Sign up

Export Citation Format

Share Document