Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting

Abstract

Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market. Stock market prices are dynamic, highly sensitive, nonlinear and chaotic. There are different techniques for forecast prices in the time-variant domain and due to variability and uncertain behavior in stock prices, traditional methods, such as data mining, statistical approaches, and non-deep neural networks models are not suited for prediction and generalized forecasting stock prices. While autoregressive fractional integrated moving average (ARFIMA) model provides a flexible tool for classes of long-memory models. The advancement of machine learningbased deep non-linear modelling confirms that the hybrid model efficiently extracts profound features and model non-linear functions. LSTM networks are a special kind of recurrent neural network (RNN) that map sequences of input observations to output observations with capabilities of long-term dependencies. A novel ARFIMA-LSTM hybrid recurrent network is presented in which ARFIMA model-based filters having the linear tendencies better than ARIMA model in the data and passes the residual to the LSTM model that captures nonlinearity in the residual values with the help of exogenous dependent variables. The model not only minimizes the volatility problem but also overcome the over fitting problem of neural networks. The model is evaluated using PSX company data of the stock market based on RMSE, MSE and MAPE along with a comparison of ARIMA, LSTM model and generalized regression radial basis neural network (GRNN) ensemble method independently. The forecasting performance indicates the effectiveness of the proposed AFRIMA-LSTM hybrid model to improve around 80% accuracy on RMSE as compared to traditional forecasting counterparts.

Investigation of singular ordinary differential equations by a neuroevolutionary approach

“…To verify the stability and robustness of the proposed technique, we got better values of global performance indicators; global mean absolute error (G MAE ) as in Eq (50) and mean of fitness values denoted as (M fit ) as in Eq (51). All results in terms of these global performance indicators, see Tables 8, 9, and 10 revealed the fact that our approach is better than state-ofthe-art approaches reported in the literature [33].…”

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

“…On the basis of above numerical study and investigation, following key findings of SEIPAHRF model for COVID-19 can be observed. In future, one may implement proposed LMANN for solving the systems representing computer virus models [35,36], prediction studies [37][38][39][40][41], nonlinear fractional differential equation [42,43], bioinformatics models [44][45][46] and financial modeling [30,47].…”

“…The strength of artificial intelligent (AI) based computing solvers has been exploited by the research community on large scale to obtain the approximated solutions of many problems arises in broad fields of applied science and technology. Some potential, recent reported studies having paramount significance including Van-der-Pol oscillatory systems, optics, electrically conducting solids, reactive transport system, nanofluidics, nanotechnology, fluid dynamics, astrophysics, circuit theory, plasma, atomic physics, bioinformatics, energy, power and functional mathematics see [24][25][26][27][28][29][30][31][32][33][34] and references cited therein. The said information is the motivational affinities to investigate in AI base numerical computing solver for the COVID-19 model.…”

Trading support method based on computational intelligence for speculators in the options market

“…The financial data of various instruments are forecasted using artificial intelligence on the basis of Long Short-term Memory (LSTM) (Cao ey al. 2019, Bukhari et al 2020, Livieris et al 2020, Gated Recurrent Unit (GRU) , Munkhdalai et al 2020, and the comparison of the forecasting accuracy of both these instruments with the popular ARMA is presented by Yamak et al 2019. The application of computational intelligence in the derivatives market is also interesting for researchers.…”