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Số người truy cập: 75,104,053

 Building a multi-output hybrid model for interval-valued time series forecasting
Tác giả hoặc Nhóm tác giả: Thuy-Linh Le, Duc Sy Nguyen, Le Nhat Hoang Tran, Vy-Tang Thi Khanh
Nơi đăng: Tạp chí Khoa học và Công nghệ Đại học Đà Nẵng; Số: 18;Từ->đến trang: 51-54;Năm: 2020
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Time series analysis and forecasting is an attractive research area over the last few years, especialy in interval-valued time series. Financial market is an example; a useful model may be of great interest to home brokers who do not possess sufficient knowledge to invest in such companies. In this paper, multi-input multi-output least square support vector regression (MIMO-LSSVR) is an improved algorithm based on support vector machine (SVM), with the combination of the sliding-window algorithm is proposed for interval-valued time series forecasting, a new branch in time series analysis field. The experiment shows MIMO-S-LSSVR positive outcomes than previous results. A retest using twotime series data sets in three years demonstrates that the proposed model is a promising alternative for interval-valued time series forecasting
ABSTRACT
Time series analysis and forecasting is an attractive research area over the last few years, especialy in interval-valued time series. Financial market is an example; a useful model may be of great interest to home brokers who do not possess sufficient knowledge to invest in such companies. In this paper, multi-input multi-output least square support vector regression (MIMO-LSSVR) is an improved algorithm based on support vector machine (SVM), with the combination of the sliding-window algorithm is proposed for interval-valued time series forecasting, a new branch in time series analysis field. The experiment shows MIMO-S-LSSVR positive outcomes than previous results. A retest using twotime series data sets in three years demonstrates that the proposed model is a promising alternative for interval-valued time series forecasting
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