Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach

C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul () and Amrit Kumar Paul
Additional contact information
C. Tamilselvi: The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
Md Yeasin: ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
Ranjit Kumar Paul: ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India
Amrit Kumar Paul: ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India

Forecasting, 2024, vol. 6, issue 1, 1-19

Abstract: Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving average (ARIMA) models. The effectiveness of a wavelet-based denoising approach was investigated on monthly wholesale price data for three major spices (turmeric, coriander, and cumin) for various markets in India. The predictive performance of these models is assessed using root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The wavelet LSTM model with Haar filter at level 6 emerged as a robust choice for accurate price predictions across all spices. It was found that the wavelet LSTM model had a significant gain in accuracy than the LSTM model by more than 30% across all accuracy metrics. The results clearly highlighted the efficacy of a wavelet-based denoising approach in enhancing the accuracy of price forecasting.

Keywords: accuracy metrics; denoising; price forecasting; machine learning; LSTM; wavelet decomposition (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/6/1/5/pdf (application/pdf)
https://www.mdpi.com/2571-9394/6/1/5/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:6:y:2024:i:1:p:5-99:d:1320288

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().