Long Short-Term Memory (LSTM) has shown significant performance on many real-world applications due to its ability to capture long-term dependencies. In this paper, we utilize LSTM to obtain a data-driven forecasting model for an application of weather forecasting. Moreover, we propose Transductive LSTM (T-LSTM) which exploits the local information in time-series prediction. In transductive learning, the samples in the test point vicinity are considered to have higher impact on fitting the model. In this study, a quadratic cost function is considered for the regression problem. Localizing the objective function is done by considering a weighted quadratic cost function at which point the samples in the neighborhood of the test point have larger weights. We investigate two weighting schemes based on the cosine similarity between the training samples and the test point. In order to assess the performance of the proposed method in different weather conditions, the experiments are conducted on two different time periods of a year. The results show that T-LSTM results in better performance in the prediction task.

中文翻译：

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用于时间序列预测的转换式LSTM：在天气预报中的应用。

长短期内存（LSTM）由于具有捕获长期依赖关系的能力，因此在许多现实世界的应用程序中均显示出显着的性能。在本文中，我们利用LSTM来获得用于天气预报的数据驱动的预报模型。此外，我们提出了转导LSTM（T-LSTM），它在时序预测中利用了本地信息。在转换学习中，测试点附近的样本被认为对拟合模型具有更高的影响。在这项研究中，回归成本考虑了二次成本函数。通过考虑加权二次成本函数来完成目标函数的定位，在该点上，测试点附近的样本具有较大的权重。我们基于训练样本和测试点之间的余弦相似性研究了两种加权方案。为了评估该方法在不同天气条件下的性能，在一年的两个不同时间段进行了实验。结果表明，T-LSTM在预测任务中具有更好的性能。