Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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Design and Application of a Slow Feature Algorithm Coupling Visual Selectivity and Multiple Long Short-Term Memory Networks

Traitement du signal ◽

10.18280/ts.380529 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1521-1530

Author(s):

Yanming Zhao ◽

Hong Yang ◽

Guoan Su

Keyword(s):

Basis Function ◽

Short Term Memory ◽

Consistency Condition ◽

Recognition Rate ◽

Component Analysis ◽

Basis Functions ◽

Good Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

In the traditional slow feature analysis (SFA), the expansion of polynomial basis function lacks the support of visual computing theories for primates, and cannot learn the uniform, continuous long short-term features through selective visual mechanism. To solve the defects, this paper designs and implements a slow feature algorithm coupling visual selectivity and multiple long short-term memory networks (LSTMs). Inspired by the visual invariance theory of natural images, this paper replaces the principal component analysis (PCA) of traditional SFA algorithm with myTICA (TICA: topologically independent component analysis) to extract image invariant Gabor basis functions, and initialize the space and series of basis functions. In view of the ability of the LSTM to learn long and short-term features, four LSTM algorithms were constructed to separately predict the long and short-term visual selectivity features of Gabor basis functions from the basis function series, and combine the functions into a new basis function, thereby solving the defect of polynomial prediction algorithms. In addition, a Lipschitz consistency condition was designed, and used to develop an approximate orthogonal pruning technique, which optimizes the prediction basis functions, and constructs a hyper-complete space for the basis function. The performance of our algorithm was evaluated by three metrics and mySFA’s classification method. The experimental results show that our algorithm achieved a good prediction effect on INRIA Holidays dataset, and outshined SFA, graph-based SFA (SFA), TICA, and myTICA in accuracy and feasibility; when the threshold was 6, the recognition rate of our algorithm was 99.98%, and the false accept rate (FAR) and false reject rate (FRR) were both smaller than 0.02%, indicating the strong classification ability of our approach.

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Airfoil buffet aerodynamics at plunge and pitch excitation based on long short-term memory neural network prediction

CEAS Aeronautical Journal ◽

10.1007/s13272-021-00550-6 ◽

2021 ◽

Author(s):

R. Zahn ◽

C. Breitsamter

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Training Data ◽

Good Prediction ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Identification Approach ◽

Long Short Term Memory ◽

Transonic Buffet

AbstractIn the present study, a nonlinear system identification approach based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. The identification approach is applied as a reduced-order modeling (ROM) technique for an efficient computation of time-varying integral quantities such as aerodynamic force and moment coefficients. Therefore, the nonlinear identification procedure as well as the generalization of the ROM are presented. The training data set for the LSTM–ROM is provided by performing forced-motion unsteady Reynolds-averaged Navier–Stokes simulations. Subsequent to the training process, the ROM is applied for the computation of the aerodynamic integral quantities associated with transonic buffet. The performance of the trained ROM is demonstrated by computing the aerodynamic loads of the NACA0012 airfoil investigated at transonic freestream conditions. In contrast to previous studies considering only a pitching excitation, both the pitch and plunge degrees of freedom of the airfoil are individually and simultaneously excited by means of an user-defined training signal. Therefore, strong nonlinear effects are considered for the training of the ROM. By comparing the results with a full-order computational fluid dynamics solution, a good prediction capability of the presented ROM method is indicated. However, compared to the results of previous studies including only the airfoil pitching excitation, a slightly reduced prediction performance is shown.

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