Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks

Kanazawa, Nobuyuki

doi:10.1016/j.jmacro.2020.103210

Cited by 5 publications

(5 citation statements)

References 73 publications

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“…A downside of their approach is that there is likely to be a lot of redundancy in the input space, and in practical real-time application, it may be wasteful to compute many predictors, only to potentially throw them away during network training. Kanazawa (2020) applies an offline radial basis function network based on Moody and Darken's technique, to macroeconomic data. He finds that the estimated impulse responses from the model, suggest that the response of macroeconomic variables to a positive supply shock, is substantially time variant.…”

Section: The Radial Basis Function Networkmentioning

confidence: 99%

Online Learning with Radial Basis Function Networks

Borrageiro¹,

Firoozye²,

Barucca³

2022

JFDS

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We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. The experiments we conduct show that there is a benefit to online transfer learning, in the form of radial basis function networks, beyond the sequential updating of recursive least-squares models. We show that the radial basis function networks, which make use of clustering algorithms to construct a kernel Gram matrix, are more beneficial than treating each training vector as separate basis functions, as occurs with kernel Ridge regression. We demonstrate quantitative procedures to determine the very structure of the radial basis function networks. Finally, we conduct experiments on the log returns of financial time series and show that the online learning models, particularly the radial basis function networks, are able to outperform a random walk baseline, whereas the offline learning models struggle to do so.

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Section: The Radial Basis Function Networkmentioning

confidence: 99%

Online Learning with Radial Basis Function Networks

Borrageiro¹,

Firoozye²,

Barucca³

2022

JFDS

View full text Add to dashboard Cite

show abstract

“…A downside of their approach is that there is likely to be much redundancy in the input space, and it seems wasteful to compute many predictors, only to potentially throw them away during network training. Kanazawa (2020) applies an offline radial basis function network based on Moody and Darken's technique to macroeconomic data. He finds that the estimated impulse responses from the model suggest that the response of macroeconomic variables to a positive supply shock is substantially time-variant.…”

Section: The Radial Basis Function Networkmentioning

confidence: 99%

Online Learning with Radial Basis Function Networks

Borrageiro¹

2021

Preprint

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We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting financial time series. We combine sequential updating with continual learning, specifically transfer learning. We perform feature representation transfer through clustering algorithms that determine the analytical structure of radial basis function networks we construct. These networks achieve lower mean-square prediction errors than kernel ridge regression models, which arbitrarily use all training vectors as prototypes. We also demonstrate quantitative procedures to determine the very structure of the networks. Finally, we conduct experiments on the log-returns of financial time series and show that these online transfer learning models outperform a random-walk baseline. In contrast, the offline learning models struggle to do so.

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“…Typically, the radial basis function network [42] (RBFN) is used for the construction of an algorithm of a type of artificial neural network (ANN), which gives radial basis algorithms the role of an activation function in forming a mathematical model that is helpful in defining linear problems of functioning inputs and neuron parameters. RBFNs have three layers of structure, including a pass-through input layer, a hidden layer, and an output layer.…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

“…RBFNs have three layers of structure, including a pass-through input layer, a hidden layer, and an output layer. Regarding the formation of the layers' structure, the input layer is formed for a vector of realistic numbers and is linked to a number of hidden neurons, and the Euclidean distance for a center vector and a Gaussian function [42] is typically used in the norm formulation since the function is radially symmetric to this vector; thus, the radial basis algorithm is named. RBFNs consider different natures of the nonlinear hidden neurons versus the linear output neuron and are justified by weights.…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

et al. 2020

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Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.

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Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks

Cited by 5 publications

References 73 publications

Online Learning with Radial Basis Function Networks

Online Learning with Radial Basis Function Networks

Online Learning with Radial Basis Function Networks

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

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