Time series forecasting is widely used in many fields including stock price forecasting, weather prediction, signal data distribution, lossless compression etc. Time series forecasting methods try to use history data and covariates data to predict the future values of the time series. Lossless compression contains two parts: a predictor and an encoder. Attention mechanism such as Transformer can be used to do the prediction part. Adding causality detection may reduce the number of data points considered and thus increase the compression rate without increment of space and running time. In this talk, we will discuss causal and attention and try to combine them on the time series prediction problem.
5月3日
11:00am - 12:00pm

地点
https://hkust.zoom.us/j/97757103798 (Passcode: 726832)
讲者/表演者
Miss Jiamin WU
主办单位
Department of Mathematics
联系方法
付款详情
对象
Alumni, Faculty and staff, PG students, UG students
语言
英语
其他活动

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研讨会, 演讲, 讲座
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Abstract
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研讨会, 演讲, 讲座
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Abstract
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