Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same g...Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.展开更多
The interior structures of planets are attracting more and more detailed attention;these studies could be of great value in improving our understanding of the early evolution of Earth. Seismological investigations of ...The interior structures of planets are attracting more and more detailed attention;these studies could be of great value in improving our understanding of the early evolution of Earth. Seismological investigations of planet interiors rely primarily on seismic waves excited by seismic events. Since tectonic activities are much weaker on other planets, e.g. Mars, the magnitudes of their seismic events are much smaller than those on Earth. It is therefore a challenge to detect seismic events on planets using such conventional techniques as short-time average/long-time average (STA/LTA) triggers. In pursuit of an effective and robust scheme to detect smallmagnitude events on Mars in the near future, we have taken Apollo lunar seismic observations as an example of weak-activity data and developed an event-detection scheme. The scheme reported here is actually a two-step processing approach: the first step involves a despike filter to remove large-amplitude impulses arising from large temperature variations;the second step employs a matched filter to unmask the seismic signals from a weak event hidden in the ambient and scattering noise. The proposed scheme has been used successfully to detect a moonquake that was not in the known moonquake catalogue, demonstrating that the two-step strategy is a feasible method for detecting seismic events on planets. Our scheme will provide a powerful tool for seismic data analysis of the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission, and China’s future lunar missions.展开更多
基金funded by the Fujian Province Science and Technology Plan,China(Grant Number 2019H0017).
文摘Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.
基金Support from the Youth Innovation Promotion Association CAS (2017094)sponsored by National Natural Science Foundation of China (grant no. 41720104006 and 41774060)
文摘The interior structures of planets are attracting more and more detailed attention;these studies could be of great value in improving our understanding of the early evolution of Earth. Seismological investigations of planet interiors rely primarily on seismic waves excited by seismic events. Since tectonic activities are much weaker on other planets, e.g. Mars, the magnitudes of their seismic events are much smaller than those on Earth. It is therefore a challenge to detect seismic events on planets using such conventional techniques as short-time average/long-time average (STA/LTA) triggers. In pursuit of an effective and robust scheme to detect smallmagnitude events on Mars in the near future, we have taken Apollo lunar seismic observations as an example of weak-activity data and developed an event-detection scheme. The scheme reported here is actually a two-step processing approach: the first step involves a despike filter to remove large-amplitude impulses arising from large temperature variations;the second step employs a matched filter to unmask the seismic signals from a weak event hidden in the ambient and scattering noise. The proposed scheme has been used successfully to detect a moonquake that was not in the known moonquake catalogue, demonstrating that the two-step strategy is a feasible method for detecting seismic events on planets. Our scheme will provide a powerful tool for seismic data analysis of the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission, and China’s future lunar missions.
