通过弱标签指导的专家混合物进行极度意识的时间序列预测
Jialou Wang1, Jacob Sanderson1, Wai Lok Woo1
1School of Computer Science, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
深度时间序列预测模型与罕见的极端斗争. 一个新的弱标志导向专家组合 (WL-MoE) 通过训练专业模型,提高了对自然灾害等关键事件的准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 深度时间序列预测模型在平均准确度方面表现出色,但在罕见,高影响的极端事件方面失败.
- 数据中的阶级不平衡将模型偏向多数模式,阻碍对自然灾害或停电等关键事件的预测.
研究的目的:
- 开发一种新的预测方法,可靠地预测罕见的,高影响的极端事件.
- 提高模型的可解释性和可听性,以便在关键场景中在现实世界中部署.
主要方法:
- 引入了一个弱标签引导的专家混合 (WL-MoE) 路由输入到专业的时间制度模型.
- 实施了两阶段的培训课程:第一阶段为专家专业化使用了弱标签,第二阶段改进了预测准确性.
- 通过专家使用配置文件启用可解释的路由,用于模型行为审计.
主要成果:
- 在七个基准数据集中,WL-MoE将平均平均平方误差 (MSE) 降低了7.9%,极端情况下的MSE降低了23.58%.
- 在英国的一项洪水预测研究中,WL-MoE将全水MSE降低了31.6%,高水MSE降低了35.0%.
结论:
- 软标签指导有效地稳定了专家专业化,提高了罕见极端事件的可靠性.
- WL-MoE方法提高了预测准确性,并为关键应用程序提供可审计的模型行为.
相关概念视频
01:26Distributions to Estimate Population Parameter
5.5K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.5K
01:03Prediction Intervals
3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.5K
01:22Estimating Population Mean with Unknown Standard Deviation
9.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
9.0K
01:10Propagation of Uncertainty from Systematic Error
1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
00:54Uncertainty: Confidence Intervals
11.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
11.9K
01:07End Point Prediction: Gran Plot
1.4K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.4K