Pattern-Wise Embedding System for Scalable Time-series Database

The neural network has proved to solve a wide range of problem. However, it is difficult to adjust the model parameter in a time-varying natural language change. Therefore, this paper proposes the pattern-wise embedding system to address this problem. The proposed approach adopts the online learning with general representation and the temporary online forecasting. To obtain proper time-invariant information, the embedding vector is extracted. Using random sampled embedding vectors, the proposed framework adapts general representations. The proposed system applies temporary weight to increase performance of the nearest task. Experiments on real-world datasets show the effectiveness of our proposed framework.