Few-shot Time-Series Forecasting with Application for Vehicular Traffic Flow
Few-shot Time-Series Forecasting with Application for Vehicular Traffic Flow
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DOI：
10.1109/iri54793.2022.00018
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Victor Tran;A. Panangadan
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文献类型：
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作者：
Victor Tran;A. Panangadan
很少有机会的机器学习试图预测输出，只给出非常少量的训练样本。大多数少数几次学习方法背后的关键思想是用来自不同但相关的数据类别的大量实例预先训练模型，这些类别有大量可供训练的实例。在使用暹罗深度学习神经网络的分类问题上，最成功的例子是少发式学习。少发式学习在时间序列预测中的应用较少。短时预测是指即使只有一小部分历史时间序列可用，也能预测时间序列的未来值。短期预测适用于长期数据不可用的领域。这项工作描述了用于小概率预测的深度神经网络结构。所有的体系结构都使用暹罗孪生网络方法来学习时间序列对之间的差异函数，而不是像传统预测模型中那样直接基于历史数据进行预测。这些网络是使用长短期记忆单元(LSTM)构建的。在预测过程中，通过使用新时间序列类型的少数可用实例作为参考输入，模型能够预测在训练数据中从未见过的时间序列类型。在加州收集的车辆交通数据的性能测量系统(PeMS)上对所提出的架构进行了评估。用在特定位置收集的交通流数据对模型进行训练，然后通过预测不同位置不同时间段(0到12小时)的交通来评估模型。以平均绝对误差(MAE)作为评价指标，同时也作为训练的损失函数。与基准最近邻预测模型相比，所提出的体系结构具有更低的预测误差。时间视界越长，预测误差越大。
Few-shot machine learning attempts to predict outputs given only a very small number of training examples. The key idea behind most few-shot learning approaches is to pre-train the model with a large number of instances from a different but related class of data, classes for which a large number of instances are available for training. Few-shot learning has been most successfully demonstrated for classification problems using Siamese deep learning neural networks. Few-shot learning is less extensively applied to time-series forecasting. Few-shot forecasting is the task of predicting future values of a time-series even when only a small set of historic time-series is available. Few-shot forecasting has applications in domains where a long history of data is not available. This work describes deep neural network architectures for few-shot forecasting. All the architectures use a Siamese twin network approach to learn a difference function between pairs of time-series, rather than directly forecasting based on historical data as seen in traditional forecasting models. The networks are built using Long short-term memory units (LSTM). During forecasting, a model is able to forecast time-series types that were never seen in the training data by using the few available instances of the new time-series type as reference inputs. The proposed architectures are evaluated on Vehicular traffic data collected in California from the Caltrans Performance Measurement System (PeMS). The models were trained with traffic flow data collected at specific locations and then are evaluated by predicting traffic at different locations at different time horizons (0 to 12 hours). The Mean Absolute Error (MAE) was used as the evaluation metric and also as the loss function for training. The proposed architectures show lower prediction error than a baseline nearest neighbor forecast model. The prediction error increases at longer time horizons.