Radial Basis Function Neural Network and Prediction of Exchange Rate

Short-term load forecasting of power system is not only the basis of scheduling of generating sets, but also the basis of working out the transaction schedule in electricity market. This paper proposes a short-term load forecasting method based on combination of radial basis function (RBF) neural network and fuzzy control, uses on-line self-modify factor fuzzy control to eliminate forecast error on the basis of RBF neural network forecasting. The practical examples show that the accuracy of short-term load forecasting and training speed can be improved and gains the very satisfactory results by the proposed method

Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to anticipate and minimize bad possibilities that arise. Method: Methods that can be used to analyze fluctuating currency exchange rate data are nonparametric regression, Nadaraya-Watson estimator, Gaussian kernel function, and Local Polynomial Regression. Results and Discussion: The results of a nonparametric regression comparison between the Nadaraya-Watson estimator, Gaussian kernel function, and local polynomial regression were obtained by MAPE of 2.508% and 0.179%, respectively. This shows that the best model uses the local polynomial regression method and predicted USD exchange rate data against IDR using the best model, namely Local polynomial Regression where the MAPE value is less than 10%, which means the prediction rate is very good. Conclusion: The nonparametric regression method of the Nadaraya-Watrson estimator, Gaussian kernel function, and local polynomial regression shows that the best model uses the local polynomial regression method.

Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the production rate of chain saw machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 carbonate rocks are accurately investigated, and the production rate of each test is measured. Some operational characteristics of the machines, i.e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i.e. production rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the production rate of chain saw machines based on the performance indices.

Aiming at the problem of low accuracy of exchange rate forecast in foreign exchange market data analysis, an exchange rate prediction model based on Lasso-IGWO-BP was proposed. Firstly, Lasso regression model is used as feature screening method of exchange rate data. Then, BP neural network is taken as the basic prediction method, and IGWO algorithm is introduced to optimise the parameters of BP neural network. Finally, the extracted features are input into the IGWO-BP model for classification prediction, thus achieving better exchange rate prediction effect. Experimental results show that compared with the Attention-LSTM, PSO-ELM and FWA-GA prediction models, the Lasso-IGWO-BP model constructed in this paper has significantly improved prediction accuracy. Therefore, exchange rate prediction model constructed can provide more reliable exchange rate data for foreign exchange market, which has certain feasibility.

The neural network data are usually characterized by abruptness, nonlinearity and time variability, and thus, it is difficult to yield accuracy results of network traffic prediction based on a traditional radial basis function neural network that has the shortcomings of slow convergence and easily falling to local optimum. To improve the accuracy of network traffic prediction and optimize the method of parameter and structure setting for a neural network, a model of radial basis function neural network with improved particle swarm optimization algorithm is proposed by referring to the related theories of network traffic and phase space reconstruction. The improved particle swarm optimization algorithm can adjust the inertia weight and the learning factors, and make $$t$$ -distribution mutation of particles’ positions via global extremum to avoid local convergence and thereby improving its global searching capacity; with such an algorithm, the parameters of radial basis function neural network are optimized; then, in order to verify the algorithm’s effectiveness, the radial basis function neural network is trained to become an optimal prediction model, which is adopted for the prediction of two typical chaotic time series and the real network traffic. It is then compared with the traditional radial basis function neural network model and the radial basis function prediction model by improved particle swarm optimization; and the simulations result shows that the application of this model improves the accuracy of network traffic prediction, and demonstrates the algorithm’s feasibility and effectiveness for network traffic prediction.

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets.

Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network

Radial basis function (RBF) neural network possesses the characteristics of fast training speed, reaching the global minimum and escaping from local minimum, et al. Under fewer samples, the RBF neural network model (RBFNN-based model) for credit comprehensive evaluation is established according to actual enterprise's credit state. For the 6 non-training enterprises, the comparison of the calculated results of several models shows that the accuracy of the RBFNN-based model established in this paper is the highest. It is effective and suitable to apply RBFNN to enterprise credit comprehensive evaluation

We propose to utilize the band spectral regression for out-of-sample forecasts of exchange rates. When one period ahead forecast is considered, there is some evidence that the band spectral regression improves its accuracy, especially when the Taylor rule fundamentals model is employed. However, when the forecasting horizon increases, the purchasing power parity (PPP) fundamentals model is found to be powerful, and we can improve the out-of-sample forecast by selecting appropriate frequency bands. Bayesian model averaging shows that placing a large weight on the business cycle frequency improves the accuracy of the out-of-sample forecasting of the PPP model (as well as the monetary fundamentals model) when a longer forecasting horizon is our focus. Without specifying the frequency bands prior to applying the regression, LASSO can provide better out-of-sample exchange rate forecasts for many cases – most patently for the PPP fundamentals model – and provide information about the dynamic relationship between forecasting variables and exchange rates. The frequency domain approach not only improves the accuracy of exchange rate forecast but provides insights for understanding why the PPP fundamentals act as a powerful predictor when the forecasting horizon increases and there is a possible improvement in the time domain regression forecast.

The purpose of this research is to explore the relationship between exchange rate and 12 financial variables in order to find useful implications to predict the movement of short-term exchange rate. As a result of using various analytical techniques such as univariate GARCH, VAR, multivariate GARCH, cointegration, and VECM, we found some valuable information on exchange rate movement. First, the KOSPI is considered as the most useful financial variable affecting short-term exchange rate changes. Second, CDS premium, S&P500, and foreign net stock buying are also meaningful variables that affect the short-term exchange rate. Third, when the Korean won depreciates, factors of instability in the domestic financial market (i.e., falling stock price, rising stock market risk, and rising country default risk) also accompany such currency depreciation. When the won appreciates, the dollar value depreciates relative to major countries’ currencies. Fourth, the volatility of the dollar index is the only variable that affects the volatility of the exchange rate. Fifth, the CDS premium is the most useful financial variable affecting long-term exchange rate changes since the CDS premium and exchange rate are in long-term equilibrium relationship. The exchange rate, not the CDS premium, adjusts for deviation from the long-term equilibrium.

(ProQuest: ... denotes formulae omitted.)1. IntroductionExchange rates, which is simply the price of one currency in terms of another currency, are one of the key component of the macroeconomics discussions in the literature. If one want to understand why some currencies depreciate and others appreciate, the factors that cause the change in supply and demand of currencies must be investigated. These factors include various fundamentals such as real income, inflation rates, real interest rates, consumer preferences, and government trade policy. For example, Purchasing Power Parity (PPP) states that a country's currency will depreciate by an amount equal to the excess of domestic inflation over foreign inflation while Uncovered Interest Parity (UIP) suggests that the interest rate differentials change the exchange rates. Traditional Flow Approach, which is also called the balance-ofpayments view, states that exchange rate moves to keep the balance of payments in equilibrium under the assumption of no government interventions. On the other hand, nominal exchange rate fluctuations should reflect movements in a country's monetary fundamentals such as relative money, output, interest rates and prices, according to the Monetary Model of Exchange Rate Determination.One of the important debates centers on the international economics is the difficulty of predicting exchange rates by using monetary fundamentals such as money supplies, outputs, and interest rates. As stated above, some theories in the economic literature claim that the exchange rates might be determined by such fundamental variables. However, since Meese and Rogoff (1983), it has been considered that exchange rates are very difficult to predict using monetary fundamentals; in particular, a simple random walk without drift (RW), is found to produce better exchange rate forecasts than fundamental-based exchange rate models. In other words, fundamental variables do not help predict future changes in exchange ratesMeese and Rogoff's (1983) finding was a shock for economists since monetary fundamentals have long been considered key determinants of exchange rates. If their finding is true, then all exchange rate models based on the fundamentals are misleading. Therefore, large numbers of studies have attempted to refute Meese and Rogoff's findings and find positive results in favor of fundamentals-based models. One of the most well-known rebuttals to Meese and Rogoff's work is Mark's (1995) study. Mark concludes in his study that monetary fundamentals contain predictive power for exchange rate. He used the long horizon regression (or Error Correction Model (ECM)) derived from the VECM (Vector Error Correction Model). However, these two studies are not the only ones testing the predictive ability of exchange rate models. Several models such as the single equation linear models, single-equation ECM, multi-equation VECM models, time-varying parameter models, and panel models have been used in the literature in the attempt to predict exchange rates. Even though most of the authors are unable to document short-run exchange rate predictability, some of them find evidence of long horizon predictability. Nevertheless, the literature has remained pessimistic about the link between exchange rates and fundamentals.The main purpose of the study is to investigate the exchange rate predictability in Turkey by using the monetary fundamentals in the ECM. This study first examines the in-sample fit of the ECM model, and then evaluates the out-of sample forecast performance of the ECM against benchmark RW model. The fundamental value of exchange rate used in this study is obtained from Frankel-Bilson Monetary Model. The error correction term is constructed under the assumptions of zero/nonzero interest rate differentials and country specific money demand elasticities to satisfy the cointegration assumption between fundamentals and exchange rates. The in-sample analysis results present evidence that monetary fundamentals are useful to explain the long horizon changes in the logarithm of the exchange rates while the out-of sample analysis results suggest that whether the ECM or the RW explains the nature of exchange rate changes might be considered that time varying. …

The prediction of exchange rate is very important for both countries and enterprises. At present, the latest prediction technology is training BP neural network through the recent exchange rate data, and it takes effect in some degree. In view of the limitations of the existing BP neural network, an improved hierarchical network model based on Long Short-Term Memory (LSTM) is proposed. The model increases the time depth of the data, and uses attention mechanism to process the historical data of different time levels, which makes the prediction ability of the model stronger. Taking the exchange rate of US dollar / rupee as an example, by comparing with the basic LSTM model and BP neural network, it is proved that the proposed model has a better effect.

As one of the important indicators of the global financial market, the fluctuation of the US dollar to Chinese yuan exchange rate not only directly affects the economic development of China and the United States, but also has important significance for analyzing the global economic situation. Analyzing the factors influencing the exchange rate between the United States dollar (USD) and the Chinese yuan (CNY) is one of the key research topics today. Researchers have found that exchange rates are influenced by factors such as national policies and the global political situation. However, there is still a certain gap in the comprehensive and objective analysis of these influencing factors. Therefore, the research topic of this article is to predict the trend of the exchange rate between the USD and the CNY and analyze the influencing factors. This study collected exchange rate data from January 2000 to November 2023 and used the ARIMA model to predict exchange rate changes. This study explores the trend of exchange rate changes by visualizing these data. Research has found that significant changes in exchange rates over the past two decades have also occurred during times of global instability and policy adjustments. The economic development level of China and the United States also directly affects the changes in exchange rates. In addition, the model used in this study also predicted the exchange rate between the USD and the CNY in 2024, providing a scientific direction for predicting the economic development trend in 2024.

Computational intelligence approaches and linear models in case studies of forecasting exchange rates

This research aims to find out the relationship between GDP, Labor, Exports and Exchange Rates to foreign direct investment in Indonesia from 1986 to 2019. Using time series data, data analysis techniques use the Error Correction Model (ECM) model, which is then followed by Eviews 10. The results of the analysis showed that the long-term calculation of GDP variables was not significant but had a positive effect, exports had a significant effect, while labor and exchange rates had a negative and insignificant effect. Furthermore, the results of short-term analysis, GDP and Exchange Rate variables are insignificant and negative value, while for Export and Labor variables are insignificant and positive value.