A compressed sensing based AI learning paradigm for crude oil price forecasting

- A compressed sensing based AI learning paradigm is proposed for crude oil price forecasting.
- The compressed sensing is used to reduce the high level of noise in original data.
- A certain artificial intelligence tool is employed to model the cleaned data.
- Empirical results statistically prove the effectiveness and robustness of the proposed method.

Due to the complexity of crude oil price series, traditional statistics-based forecasting approach cannot produce a good prediction performance. In order to improve the prediction performance, a novel compressed sensing based learning paradigm is proposed through integrating compressed sensing based denoising (CSD) and certain artificial intelligence (AI), i.e., CSD-AI. In the proposed learning paradigm, CSD is first performed as a preprocessor for the original data of international crude oil price to eliminate the noise, and then a certain powerful AI tool is employed to conduct prediction for the cleaned data. In particular, the process of CSD aims to reduce the level of noise which pollutes the data, and to further enhance the prediction performance of the AI model. For verification purpose, international crude oil price series of West Texas Intermediate (WTI) are taken as sample data. Empirical results demonstrate that the proposed CSD-AI learning paradigm significantly outperforms all other benchmark models including single models without CSD process and hybrid models with other denoising techniques, in terms of level and directional accuracies. Furthermore, in the case of different data samples with different time ranges, the proposed model performs the best, indicating that the proposed CSD-AI learning paradigm is an effective and robust approach in crude oil price prediction.