Forecasting financial time series through intrinsic dimension estimation and non-linear data projection

1999 ◽  
pp. 596-605 ◽  
Author(s):  
M. Verleysen ◽  
E. de Bodt ◽  
A. Lendasse
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Intrinsic Dimension ◽  
Dimension Estimation ◽  
Financial Time ◽  
Non Linear

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

scholarly journals Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

2021 ◽  
Vol 9 (2) ◽  
pp. 18
Author(s):  
Katleho Makatjane ◽  
Ntebogang Moroke
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Linear Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Financial Time ◽  
Johannesburg Stock Exchange ◽  
Non Linear

During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated SARIMA(2,1,0)×(2,1,0)240−MS(2)−EGARCH(1,1)−GEVD proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

scholarly journals Non-Linear Diffusion and Power Law Properties of Heterogeneous Systems: Application to Financial Time Series

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 649 ◽  
Author(s):  
Miguel Fuentes
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Power Law ◽  
Financial Time Series ◽  
Heterogeneous Systems ◽  
Diffusive Transport ◽  
Linear Diffusion ◽  
Financial Time ◽  
Markovian Processes ◽  
Non Linear

In this work, we show that it is possible to obtain important ubiquitous physical characteristics when an aggregation of many systems is taken into account. We discuss the possibility of obtaining not only an anomalous diffusion process, but also a Non-Linear diffusion equation, that leads to a probability distribution, when using a set of non-Markovian processes. This probability distribution shows a power law behavior in the structure of its tails. It also reflects the anomalous transport characteristics of the ensemble of particles. This ubiquitous behavior, with a power law in the diffusive transport and the structure of the probability distribution, is related to a fast fluctuating phenomenon presented in the noise parameter. We discuss all the previous results using a financial time series example.

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