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首页> 外文会议>International Conference on Artificial Neural Networks >Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates
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Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates

机译：具有置信度估计的高变异性时间序列的稀疏递归混合密度网络

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Accurate forecasting of a high variability time series has relevance in many applications such as supply-chain management, price prediction in stock markets and demand forecasting in the energy segment. Most often forecasts of such time series depend on many factors ranging from weather to socio-economic attributes such as GDP or average income. Dependence on such features can cause the underlying time series to be highly variable in nature and possess non-stationary shifts. Most traditional forecasting methods fail to capture such trend shifts and high variability present in the data. Further, for certain applications, it may be necessary to estimate the confidence of the forecasts. In this work, we propose two variants of recurrent mixture density network (RMDN), for time series forecasting, that have the ability to handle high-dimensional input features, capture trend shifts and high variability present in the data, and provide a confidence estimate of the forecast. To this end, we first pass the high-dimensional time series data through a feedforward layer, which performs dimensionality reduction or feature selection in an unsupervised manner by inducing sparsity on the weights of the feedforward layer. The resultant low-dimensional time series is then fed through recurrent layers to capture temporal patterns. These recurrent layers also aid in learning the latent representation of the input data. Thereafter, a mixture density network (MDN) is used to model the variability and trend shifts present in the input and it also estimates the confidence of the predictions. The models are trained in an end-to-end fashion and the efficacy of the proposed models is demonstrated on three publicly available datasets from energy markets.
机译：高可变时间序列的准确预测在许多应用中都具有相关性，例如供应链管理，股票市场中的价格预测以及能源领域中的需求预测。通常，对此类时间序列的预测取决于许多因素，从天气到社会经济属性（例如GDP或平均收入）不等。对此类特征的依赖可能导致基础时间序列本质上具有很大的可变性，并具有非平稳的偏移。大多数传统的预测方法都无法捕获数据中存在的趋势变化和高变异性。此外，对于某些应用，可能有必要估计预测的置信度。在这项工作中，我们为时间序列预测提出了两种递归混合密度网络（RMDN）的变体，它们能够处理高维输入特征，捕获数据中存在的趋势变化和高可变性并提供置信度估计的预测。为此，我们首先将高维时间序列数据传递到前馈层，该层通过在前馈层的权重上引入稀疏性，以无监督的方式执行降维或特征选择。然后将所得的低维时间序列馈入递归层，以捕获时间模式。这些循环层还有助于学习输入数据的潜在表示形式。此后，使用混合密度网络（MDN）对输入中存在的可变性和趋势转移进行建模，并且还估计预测的置信度。以端到端的方式对模型进行了训练，并在来自能源市场的三个公开可用的数据集上证明了所提出模型的有效性。

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