加速基於深度學習的時間序列預測

Abstract

時間序列預測在許多領域是重要的研究問題，包含金融市場、天氣、電力消耗 以及交通阻塞。然而近期的研究使用機器學習訓練的時間非常長，需要使用複雜 的模型。因此針對這個問題，我們在這篇論文提出一個簡化的深度學習的模型以 達到有效率的表現。我們的模型單純基於卷積神經網路，並以此學習長期以及短 期的資訊。本研究總共在七個資料集上進行實驗，能在訓練時間上大幅贏過目前 最新的模型，且能獲得近乎相同甚至更好的準確率。

Time series forecasting is an important research area across many domains, such as predictions of financial market, weather, electricity consumption, and traffic jam situation. However, most of recent works are usually time-consuming and complex. In this paper, we propose a deep learning model to tackle this issue, and deliver efficient performance. Our model uses purely Convolutional Neural Network (CNN) structure to capture both long-term and short-term features. Thorough empirical studies based upon the total seven different dataset demonstrate that the our model can outperform state-of-the-art methods over training time with comparable performance.