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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1862
Title: Local temporal feature for timeseries prediction
Authors: Tran Ngoc Duy Chuong 
Keywords: Air quality data management;Time series data
Issue Date: 2024
Abstract: 
Gaps in time series data pose significant challenges for deep learning research, impacting the performance of predictive models. This thesis proposes a novel hybrid model to address these challenges by combining one-dimensional convolutional layers (Conv1D), inspired by the Inception architecture, with Long Short-Term Memory (LSTM) units. This innovative model leverages the spatial feature extraction capabilities of Conv1D and the temporal sequence learning strengths of LSTM to improve prediction accuracy.Our model was rigorously tested on datasets characterized by extensive gaps and dense data points, specifically focusing on air quality measurements at Vietnamese German University. The results demonstrate the model’s superior ability to handle low-quality time series data without the need for manual adjustments specific to each dataset, highlighting its robustness and versatility
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1862
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Computer Science (CS)

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