[Retracted] Exchange Rate Forecasting Based on Deep Learning and NSGA‐II Models

Abstract

Today, the global exchange market has been the world’s largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA-II-based dual-objective measurement optimization algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA-II-based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision-making in the exchange market.

Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction

“…As a result of the accumulated errors based on a single model, the freshly trained models beat the next-day model, according to the results [10].The two additional methods used to forecast the exchange rates are Deep learning and the Nsga-II based dual-objective measurement optimization algorithm. The deep learning model has more accurate findings when compared to more conventional traditional exchange rate prediction algorithms additionally optimizing the selection of investment portfolios is the Nsga-II-based model, which recommend shareholders a more suitable savings portfolio plan [11]. In this study, the author coupled economic models that outperform the random walk model with contemporary machine learning approaches.…”

A Proposal of Transfer Learning for Monthly Macroeconomic Time Series Forecast

“…Besides LSTM, other recurrent neuronal networks have been used as a gated recurrent unit [15]. Some researchers have used non-recurrent networks such as Fully Connected Architecture, Convolutional Neuronal Networks with residual connections [21], Neural Network Autoregression (NNAR) models [10], multilayer perceptron (MLP) [15], and stacked autoencoders [8].…”

“…For example, [5] found that LSTM outperformed VAR and SVM for predicting the monthly USD-INR foreign exchange rate. In [8] they concluded that stacked autoencoders achieve more accurate results than support vector machines for predicting the 0 daily EUR-USD exchange rate. In [2] they found that LSTM had the best performance for the inflation prediction of more than one month compared to random forests, extreme gradient boosting, and k-nearest neighbors.…”

“…In recent years, deep learning models have gained relevance in the time series field because they have shown high prediction capacity in several tasks, like the forecasting of electricity consumption [6], weather conditions [7] and other tasks. Nevertheless, its implementation in macroeconomic forecasts has been scarce [8][9][10][11]. A possible reason is that deep learning performs better with large datasets and some macroeconomic variables have a low number of observations [4], especially monthly or quarterly time series.…”

Retracted: Exchange Rate Forecasting Based on Deep Learning and NSGA‐II Models

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”