Data Science Analysis Method Design via Big Data Technology and Attention Neural Network

Abstract

Because of the rapid expansion of big data technology, time series data is on the rise. These time series data include a lot of hidden information, and mining and evaluating hidden information is very important in finance, medical care, and transportation. Time series data forecasting is a data science analysis application, yet present time series data forecasting models do not completely account for the peculiarities of time series data. Traditional machine learning algorithms extract data features through artificially designed rules, while deep learning learns abstract representations of data through multiple processing layers. This not only saves the step of manually extracting features, but also greatly improves generalization performance for model. Therefore, this work utilizes big data technology to collect corresponding time series data and then uses deep learning to study the problem of time series data prediction. This work proposes a time series data prediction analysis network (TSDPANet). First, this work improves the traditional Inception module and proposes a feature extraction module suitable for 2D time series data. In 2D convolution, this solves the inefficiency of time series. Second, the notion of feature attention method for time series features is proposed in this study. The model focuses the neural network's data on the effectiveness of several measures. The feature attention module is used to assign different weights to different features according to their importance, which can effectively enhance and weaken the features. Third, this work conducts multi-faceted experiments on the proposed method.