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Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach 去噪能否提高学习模型的预测精度？小波分解法案例

Forecasting Pub Date : 2024-01-16 DOI: 10.3390/forecast6010005

C. Tamilselvi, M. Yeasin, R. Paul, Amrit Kumar Paul

{"title":"Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach","authors":"C. Tamilselvi, M. Yeasin, R. Paul, Amrit Kumar Paul","doi":"10.3390/forecast6010005","DOIUrl":"https://doi.org/10.3390/forecast6010005","url":null,"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.","PeriodicalId":508737,"journal":{"name":"Forecasting","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139619437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning 利用两级信号分解和深度学习对阿拉斯加永久冻土带的气温进行预测分析

Forecasting Pub Date : 2024-01-09 DOI: 10.3390/forecast6010004

Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance, T. Pasch

{"title":"Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning","authors":"Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance, T. Pasch","doi":"10.3390/forecast6010004","DOIUrl":"https://doi.org/10.3390/forecast6010004","url":null,"abstract":"Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.","PeriodicalId":508737,"journal":{"name":"Forecasting","volume":"94 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models 通过结合 ARIMA 模型中的振荡改善对 COVID-19 大流行传播的预测

Forecasting Pub Date : 2023-12-26 DOI: 10.3390/forecast6010002

Eunju Hwang

引用次数: 0

Advancements in Downscaling Global Climate Model Temperature Data in Southeast Asia: A Machine Learning Approach 东南亚全球气候模型气温数据降尺度研究进展：机器学习方法

Forecasting Pub Date : 2023-12-20 DOI: 10.3390/forecast6010001

Teerachai Amnuaylojaroen

{"title":"Advancements in Downscaling Global Climate Model Temperature Data in Southeast Asia: A Machine Learning Approach","authors":"Teerachai Amnuaylojaroen","doi":"10.3390/forecast6010001","DOIUrl":"https://doi.org/10.3390/forecast6010001","url":null,"abstract":"Southeast Asia (SEA), known for its diverse climate and broad coastal regions, is particularly vulnerable to the effects of climate change. The purpose of this study is to enhance the spatial resolution of temperature projections over Southeast Asia (SEA) by employing three machine learning methods: Random Forest (RF), Gradient Boosting Machine (GBM), and Decision Tree (DT). Preliminary analyses of raw General Circulation Model (GCM) data between the years 1990 and 2014 have shown an underestimation of temperatures, which is mostly due to the insufficient amount of precision in its spatial resolution. Our findings show that the RF method has a significant concordance with high-resolution observational data, as evidenced by a low mean squared error (MSE) value of 2.78 and a high Pearson correlation coefficient of 0.94. The GBM method, while effective, had a broader range of predictions, indicated by a mean squared error (MSE) score of 5.90. The Decision Tree (DT) method performed the best, with the lowest mean squared error (MSE) value of 2.43, which closely matched the actual data. The first General Circulation Model (GCM) data, on the other hand, exhibited significant forecast errors, as evidenced by a mean squared error (MSE) value of 7.84. The promise of machine learning methods, notably the Random Forest (RF) and Decision Tree (DT) algorithms, in improving temperature predictions for the Southeast Asian region is highlighted in the present study.","PeriodicalId":508737,"journal":{"name":"Forecasting","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess 分解与征服：利用黄土进行多季节趋势分解的时间序列预测

Forecasting Pub Date : 2023-12-12 DOI: 10.3390/forecast5040037

Amirhossein Sohrabbeig, Omid Ardakanian, Petr Musilek

引用次数: 0

Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models 探索在线课程在 COVID 中的作用--19 供应链领域的危机管理--使用模糊认知图（FCM）模型进行预测

Forecasting Pub Date : 2023-11-20 DOI: 10.3390/forecast5040035

Dimitrios K. Nasiopoulos, Dimitrios A. Arvanitidis, Dimitrios M. Mastrakoulis, N. Kanellos, Thomas Fotiadis, Dimitrios E. Koulouriotis

{"title":"Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models","authors":"Dimitrios K. Nasiopoulos, Dimitrios A. Arvanitidis, Dimitrios M. Mastrakoulis, N. Kanellos, Thomas Fotiadis, Dimitrios E. Koulouriotis","doi":"10.3390/forecast5040035","DOIUrl":"https://doi.org/10.3390/forecast5040035","url":null,"abstract":"Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disrupt the flow of goods or several planned actions. For this reason, increased resilience against various types of risks that threaten the viability of an organization is of major importance. One of the ways to determine the magnitude of the risk an organization runs is to measure how popular it is with the buying public. Although risk is impossible to eliminate, effective forecasting and supply chain risk management can help businesses identify, assess, and reduce it. As a result, good supply chain risk management, including forecasting, is critical for every company. To measure the popularity of an organization, there are some discrete values (bounce rate, global ranking, organic traffic, non-branded traffic, branded traffic), known as KPIs. Below are some hypotheses that affect these values and a model for the way in which these values interact with each other. As a result of the research, it is clear how important it is for an organization to increase its popularity, to increase promotion in the shareholder community, and to be in a position to be able to predict its future requirements.","PeriodicalId":508737,"journal":{"name":"Forecasting","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139259657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0