Improving Forecasting Performance for Abnormal Time Series Data with the TFT-TPE Integrated Model and Google Trends

• Published in: Cybernetics and information technologies : CIT Vol. 25; no. 2; pp. 152 - 172
• Main Authors: Son, Ngo Van, Nhat, Vo Viet Minh
• Format: Journal Article
• Language: English
• Published: Sofia Sciendo 01.06.2025; De Gruyter Poland
• Subjects: Abnormal time series data; Google Trends; TFT-TPE integration; Tourism demand forecasting; Transformer
• ISSN: 1314-4081; 1311-9702; 1314-4081
• DOI: 10.2478/cait-2025-0017

Forecasting is essential in manufacturing and business, but is hindered by abnormal events like COVID-19. This paper proposes a model that integrates Temporal Fusion Transformer (TFT) with Tree-Structured Parzen Estimator (TPE), in which TFT is a deep neural network specifically designed for processing time series data to capture trends and model complex data variations and, at the same time, TPE is an optimization technique that uses a tree-like data structure to determine the best set of hyperparameters for TFT. The TFT-TPE integrated model, therefore, provides an effective solution to the forecasting problem, especially for abnormal data. The study proposes a combination of forecasting historical data, considering the COVID-19 period, and utilizing Google Trends to enhance forecasting accuracy. The experimental results show that the TFT-TPE integrated model achieves forecasting results better than traditional forecasting models, especially the ability to overcome the anomalies in time series data.