Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr, NeuralProphet, Seq2Seq have been explored for time series forecasting problem. In this paper, we propose a novel time series forecast model, DeepGB. We formulate and implement a variant of Gradient boosting wherein the weak learners are DNNs whose weights are incrementally found in a greedy manner over iterations. In particular, we develop a new embedding architecture that improves the performance of many deep learning models on time series using Gradient boosting variant. We demonstrate that our model outperforms existing comparable state-of-the-art models using real-world sensor data and public dataset.

翻译：时间序列预测是来自不同数据驱动应用程序的一项基本任务。 许多先进的自动递减方法, 如 ARIMA 被用于开发预测模型。 最近, 探索了深亚、 神经质素、 Seq2Seqeq 等深学习基础方法, 以解决时间序列预测问题 。 在本文中, 我们提出一个新的时间序列预测模型, DeepGB 。 我们制定并实施了一个渐进式推升变式, 使弱学习者是DNN, 其重量在迭代中逐渐被贪婪地发现。 特别是, 我们开发了一个新的嵌入式结构, 改进了许多使用梯度推动变异的时序深学习模型的性能。 我们用现实世界传感器数据和公共数据集, 展示了我们的模型比现有可比的状态模型更优。