Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting
Accurate time-series forecasting plays a vital role in sensor-driven applications such as energy monitoring, traffic flow prediction, and environmental sensing. While most existing approaches focus on extracting local patterns from historical observations, they often overlook the global temporal inf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4590 |
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| author | Jiahe Yan Honghui Li Yanhui Bai Jie Liu Hairui Lv Yang Bai |
| author_facet | Jiahe Yan Honghui Li Yanhui Bai Jie Liu Hairui Lv Yang Bai |
| author_sort | Jiahe Yan |
| collection | DOAJ |
| description | Accurate time-series forecasting plays a vital role in sensor-driven applications such as energy monitoring, traffic flow prediction, and environmental sensing. While most existing approaches focus on extracting local patterns from historical observations, they often overlook the global temporal information embedded in timestamps. However, this information represents a valuable yet underutilized aspect of sensor-based data that can significantly enhance forecasting performance. In this paper, we propose a novel timestamp-guided knowledge distillation framework (TKDF), which integrates both historical and timestamp information through mutual learning between heterogeneous prediction branches to improve forecasting robustness. The framework comprises two complementary branches: a Backbone Model that captures local dependencies from historical sequences, and a Timestamp Mapper that learns global temporal patterns encoded in timestamp features. To enhance information transfer and reduce representational redundancy, a self-distillation mechanism is introduced within the Timestamp Mapper. Extensive experiments on multiple real-world sensor datasets—covering electricity consumption, traffic flow, and meteorological measurements—demonstrate that the TKDF consistently improves the performance of mainstream forecasting models. |
| format | Article |
| id | doaj-art-e2a3305d7ab84f1c9a2af95f4e6f3fbe |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e2a3305d7ab84f1c9a2af95f4e6f3fbe2025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-07-012515459010.3390/s25154590Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series ForecastingJiahe Yan0Honghui Li1Yanhui Bai2Jie Liu3Hairui Lv4Yang Bai5School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, ChinaInner Mongolia High Tech Holdings Co., Ltd., Ordos 017000, ChinaInner Mongolia High Tech Holdings Co., Ltd., Ordos 017000, ChinaAccurate time-series forecasting plays a vital role in sensor-driven applications such as energy monitoring, traffic flow prediction, and environmental sensing. While most existing approaches focus on extracting local patterns from historical observations, they often overlook the global temporal information embedded in timestamps. However, this information represents a valuable yet underutilized aspect of sensor-based data that can significantly enhance forecasting performance. In this paper, we propose a novel timestamp-guided knowledge distillation framework (TKDF), which integrates both historical and timestamp information through mutual learning between heterogeneous prediction branches to improve forecasting robustness. The framework comprises two complementary branches: a Backbone Model that captures local dependencies from historical sequences, and a Timestamp Mapper that learns global temporal patterns encoded in timestamp features. To enhance information transfer and reduce representational redundancy, a self-distillation mechanism is introduced within the Timestamp Mapper. Extensive experiments on multiple real-world sensor datasets—covering electricity consumption, traffic flow, and meteorological measurements—demonstrate that the TKDF consistently improves the performance of mainstream forecasting models.https://www.mdpi.com/1424-8220/25/15/4590time-series forecastingsensor dataknowledge distillationtimestamp modelingself-distillation |
| spellingShingle | Jiahe Yan Honghui Li Yanhui Bai Jie Liu Hairui Lv Yang Bai Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting Sensors time-series forecasting sensor data knowledge distillation timestamp modeling self-distillation |
| title | Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting |
| title_full | Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting |
| title_fullStr | Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting |
| title_full_unstemmed | Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting |
| title_short | Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting |
| title_sort | timestamp guided knowledge distillation for robust sensor based time series forecasting |
| topic | time-series forecasting sensor data knowledge distillation timestamp modeling self-distillation |
| url | https://www.mdpi.com/1424-8220/25/15/4590 |
| work_keys_str_mv | AT jiaheyan timestampguidedknowledgedistillationforrobustsensorbasedtimeseriesforecasting AT honghuili timestampguidedknowledgedistillationforrobustsensorbasedtimeseriesforecasting AT yanhuibai timestampguidedknowledgedistillationforrobustsensorbasedtimeseriesforecasting AT jieliu timestampguidedknowledgedistillationforrobustsensorbasedtimeseriesforecasting AT hairuilv timestampguidedknowledgedistillationforrobustsensorbasedtimeseriesforecasting AT yangbai timestampguidedknowledgedistillationforrobustsensorbasedtimeseriesforecasting |