Attention-Based Multi-Scale Prediction Network for Time-Series Data

Time series data is a kind of data accumulated over time, which can describe the change of phenomenon. This kind of data reflects the degree of change of a certain thing or phenomenon. The existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction, but they are not enough to mine the periodicity of data. In this article, we perform periodic analysis on two types of time series data, select time metrics with high periodic characteristics, and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data. A loss calculation method for traffic time series characteristics is proposed as well. Multiple experiments have been conducted on actual data sets. The experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods (ARIMA, LSTM, etc.) and 3%-5% increase on MAPE.

Automated summary

This article explores time series data and proposes a multi-scale prediction model based on the attention mechanism for the periodic trend of the data. Experimental results demonstrate its superior performance compared to traditional methods.