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时间序列预测的后门攻击是新兴的领域，存在很多探索的方向。我们在这里提供一些思路。除了在追求更高效和隐蔽的触发器之外，还有以下攻击问题没有解决。首先，能否后门攻击时间序列缺失值推理任务（time series imputation）。当前的 BackTime 利用触发器和目标模式的顺序时间链接来实现攻击。但是推理任务需要同时考虑缺失值之...
代码链接:github.com/liuwj2000/TI 关键词:multivariate time-series imputation, disentangled representation 研究方向:时间序列缺失值问题 一句话总结全文:我们提出了一种基于矩阵分解的多元时间序列插补模型,该模型包含有意义的解缠结时间表示,可解释多个解释因素(趋势、季节性、局部偏差)。 研究内容:多元时间序列经常面...
3.Xgboost predict: 基本上大概的走势已经被date_trend和hour_trend决定了,剩下就是研究这个travel_time如何围绕这两个trends上下变化的,我们使用非线性的xgboost来训练,关于时间的feature非常简单,基本上为minute, hour, day, week_day, month, vacation, 其他关于的路本身的feature后面再讲,训练的数据train_df 为t...
deep-learningtime-serieslocationspatio-temporaldemand-forecastingprobabilistic-modelsspatio-temporal-dataanomaly-detectiontraffic-predictionspatio-temporal-modelingaccident-detectionmultivariate-timeseriestime-series-predictionspatio-temporal-predictiontime-series-forecastingpaper-listtime-series-imputationtravel-time-predictio...
github:ChunjingXiao/DiffAD: Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models (github.com) arxiv: 基于条件权重增量扩散模型的时间序列异常检测 摘要 现有的时间序列异常检测模型主要是针对正常点占主导地位的数据进行训练,在某些时刻异常点密集出现时会变得无效。为了...
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TS
AutoMTS coherently supports an extensive set state-of-the-art methods for (multivariate) time series imputation and outlier detection-and-treatment, considering both point and segment/serial occurrences. A comprehensive evaluation of AutoMTS is accomplished using heterogeneous sensors from two water ...
Set up Azure Machine Learning automated machine learning (AutoML) to train time-series forecasting models with the Azure Machine Learning CLI and Python SDK.
Set up Azure Machine Learning automated machine learning (AutoML) to train time-series forecasting models with the Azure Machine Learning CLI and Python SDK.