Table 6.
RNN in economics
| Article | Aim of study | Specific approach | Benchmark methods for comparison | Superiority of the proposed method |
|---|---|---|---|---|
| (Alsmadi et al., 2020) | Predict helpful reviews | RCNN | Fasttext, SVM, Bi-LSTM, (3 Layers) CNN | Outperform conventional as well as deep learning-based models in classification accuracy |
| (Becerra-Vicario et al., 2020) | Predict bankruptcy | DRCNN, LOGIT | Single DRCNN, single LOGIT, neural network | Predict well |
| (Ebrahimi et al., 2020) | Identify semi-supervised cyber threat | Transductive SVMs, LSTM | k-NN, LOGI, RF, SVM, CNN, LSTM, Transductive SVM | State-of-the-art classification performance |
| (Agarwal et al., 2021) | Detect Fraudulent resource consumption attack | LSTM | DTC, RFC, LR, SVM, KNN, ANN | Perform best in effectively and accurately detecting FRC attacks |
| (Arkhangelski et al., 2020) | Evaluate the economic benefits | LSTM | MILP, fuzzy logic, or another linear optimization technique | More prediction accuracy |
| (Haytamy & Omara, 2020) | Predict the Cloud QoS provisioned values | LSTM, PSO | MQPM | Outperforms the existing MQPM model in terms of RMSE |
| (Andrijasa, 2019) | Predict exchange currency rates | Encoder-decoder RNN | - | - |
| (Tang et al., 2020) | Forecast economic recession through Share Price | LSTM | MA, KNN, ARIMA, Prophet | LSTM outperforms the other models in prediction |
| (Zhang et al., 2020a) | Forecast day-ahead electricity price | DRNN | Single SVM, hybrid SVM network | Outperform in terms of simulating the relationships between external factors and the electricity price |
| (Zhang et al., 2019) | Promote the accuracy of wind prediction | LSTM, multi-objective PSO | Grey Model | Numeric results demonstrate that LSTM is superior to the traditional grey model in terms of prediction accuracy, robustness, and computational efficiency |
| (Zhou et al., 2019) | Forecast electricity price | LSTM, SMBO | SVR, BPNN, GTB, DTR, LSTM series models include shallow LSTM, stacked LSTM, EEMD-LSTM and EEMD-LSTM-SMBO | Much better than that of the general LSTM model and traditional models in accuracy and stability |
| (Abdel-Nasser & Mahmoud, 2019) | Forecast photovoltaic power | LSTM-RNN | MLR, BRT, and neural networks | Further reduction in the forecasting error |
| (Guo et al., 2018) | Forecast short-term power load | Integrating several LSTM networks | LSTM, ARIMA, SVR, MLP | Improve the forecasting performance |
| (Mishev et al., 2020) | Analyze sentiment in finance | RNN, RNN-Attention, CNN, Dense Network | SVC, XGB, Dense, CNN, RNN | Better performance in several criteria |
| (Ji et al., 2021) | Forecast stock indices | IPSO and LSTM | Support-vector regression, LSTM and PSO-LSTM | High reliability and good forecasting capability |
| (Jin et al., 2020) | Predict stock closing price | LSTM, sentiment analysis, attention mechanism, empirical modal decomposition |
LS_RF, S_LSTM, The LSTM model that considers the S_AM_LSTM |
The highest accuracy, the lowest time offset and the closest predictive value when predicting the stock market |
| (Nikou et al., 2019) | Predict stock price | LSTM | ANN, RF, SVM | Better in prediction of the close price of iShares MSCI United Kingdom than the other methods |
| (Niu et al., 2020) | Predict stock price index | VMD-LSTM | PNN, ELM, CNN, and LSTM, and the hybrid models EMD-BPNN, EMD-ELM, EMD-CNN, EMD-LSTM, VMD-BPNN, VMD-ELM, and VMD-CNN | The hybrid models perform significantly better than the single models |
| (Sharaf et al., 2021) | Predict stock price | LSTM, CNN, Stacked-LSTM, and Bidirectional-LSTM | SVM, Linear Regression, LOGIT, K-Neighbors, Decision Tree, RF | Outperform the other models based on several evaluation metrics |
| (Katayama et al., 2019) | Identify sentiment polarity in financial news | LSTM, Convolution model | Common polarity dictionary | Captures more news sentiment |
| (Tao et al., 2020) | Evaluate the impact of the Northridge Earthquake | LSTM, NAR neural network | Single LSTM, single NAR | Perform better based on some criteria |
| (He, 2021) | Predict investment benefits and national economic attributes | EEMD-LSTM | BP model, EEMD-BP model, LSTM model, and ARMA | Highest prediction accuracy |
| (Wu et al., 2018) | Estimate remaining useful life of complex engineered systems | Vanilla LSTM | RNN, GRU | Significance of performance improvement |
| (Sehovac & Grolinger, 2020) | Forecast electrical load | S2S RNN | Vanilla RNN, LSTM, and GRU | Outperform other models |
| (Li et al., 2021) | Predict the price of gold | VMD-ICSS-BiGRU | SVR, LR, ANN, LSTM | Consistently reduce the forecasting error and improve the fitting performance effectively |
Note: RCNN (Recurrent Convolutional Neural Network), Bi-LSTM (Bidirectional-LSTM), DRCNN (Deep Recurrent Convolutional Neural Network), DRNN (Deep Recurrent Neural Network), SVC (Support Vector Classifier), XGB (Extreme Gradient Boosting), IPSO (Improved Particle Swarm Optimization), LS_RF (Random Rorest estimates using LSboost), S_LSTM (The LSTM model considering the sentiment index), S_AM_LSTM (The LSTM model that considers the sentiment index and attention mechanism), EMD (Empirical Modal Decomposition), NAR (Nonlinear Autoregressive), DTC (Decision Tree Classifier), RFC (Random Forest Classifier), MINLP (Mixed-Integer Nonlinear Programming), MQPM (Multivariate Quality of service Prediction Model), SMBO (Sequence Model-Based Optimization), GTB (Gradient Boosting Regressor), DTR (Decision tree regressor), EEMD (Ensemble Empirical Mode Decomposition), MLR (Multiple Linear Regression), BRT (Bagged Regression Trees), MA (Moving Average), ARMA (Autoregressive Moving Average), S2S RNN (Sequence to Sequence Recurrent Neural Network), ICSS (Iterated Cumulative Sums of Squares), BiGRU (Bidirectional Gated Recurrent Unit), GRU (Gated Recurrent Unit), LR (Linear Regression)