Financial Time Series Forecasting with the Deep Learning Ensemble Model
Abstract
:1. Introduction
2. Literature Review
3. The ARMA-CNNLSTM Ensemble Forecasting Model
3.1. Ensemble Forecasting Model
3.2. Individual Ensemble Models
4. Empirical Studies
4.1. Data Description and Statistical Tests
4.2. Results for In-Sample Model Fit
4.3. Results for Out-of-Sample Model Performance Evaluation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | Mean | Min | Max | Standard Deviation | Skewness | Kurtosis | ||
---|---|---|---|---|---|---|---|---|
P | 12.32 | 2.97 | 30.52 | 7.40 | 0.77 | 2.34 | 0 | 0.4685 |
P | 2801.7 | 1950.01 | 5166.35 | 529.38 | 0.75 | 4.53 | 0 | 0.001 |
P | 3028.11 | 4.22 | 19187 | 3847.12 | 1.15 | 3.25 | 0 | 0.5312 |
Model | RMSE | MAPE | MAE | |
---|---|---|---|---|
Random walk | 1.2399 | 0.0415 | 0.9151 | 0.4651 |
ARMA | 1.2379 | 0.0413 | 0.9122 | 0.5581 |
MLP | 1.3771 | 0.0466 | 1.0217 | 0.5039 |
LSTM | 3.9867 | 0.1552 | 3.3696 | 0.5504 |
CNN | 1.7748 | 0.0621 | 1.3474 | 0.4884 |
ARMA-CNNLSTM | 1.2195 | 0.0400 | 0.8837 | 0.6047 |
Model | RMSE | MAPE | MAE | |
---|---|---|---|---|
Random walk | 1.7173 | 5.6231 | 1.271 | 0.3517 |
ARMA | 1.2004 | 1.3853 | 0.8669 | 0.7526 |
MLP | 1.2175 | 1.2291 | 0.8727 | 0.7321 |
LSTM | 1.2022 | 1.0637 | 0.8655 | 0.7464 |
CNN | 1.2061 | 1.2057 | 0.8679 | 0.7403 |
ARMA-CNNLSTM | 1.1964 | 1.1479 | 0.861 | 0.7423 |
Model | RMSE | MAPE | MAE | |
---|---|---|---|---|
Random walk | 323.8311 | 0.0257 | 199.1424 | 0.5314 |
ARMA | 324.6788 | 0.0258 | 199.5287 | 0.4928 |
MLP | 341.0648 | 0.028 | 217.3472 | 0.5153 |
LSTM | 476.8439 | 0.0423 | 327.0795 | 0.5395 |
CNN | 378.66 | 0.0315 | 243.013 | 0.5298 |
ARMA-CNNLSTM | 323.7705 | 0.0254 | 197.04 | 0.5556 |
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He, K.; Yang, Q.; Ji, L.; Pan, J.; Zou, Y. Financial Time Series Forecasting with the Deep Learning Ensemble Model. Mathematics 2023, 11, 1054. https://doi.org/10.3390/math11041054
He K, Yang Q, Ji L, Pan J, Zou Y. Financial Time Series Forecasting with the Deep Learning Ensemble Model. Mathematics. 2023; 11(4):1054. https://doi.org/10.3390/math11041054
Chicago/Turabian StyleHe, Kaijian, Qian Yang, Lei Ji, Jingcheng Pan, and Yingchao Zou. 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model" Mathematics 11, no. 4: 1054. https://doi.org/10.3390/math11041054
APA StyleHe, K., Yang, Q., Ji, L., Pan, J., & Zou, Y. (2023). Financial Time Series Forecasting with the Deep Learning Ensemble Model. Mathematics, 11(4), 1054. https://doi.org/10.3390/math11041054