Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model.

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time.Usually,vaccine production preparation must be done at least six months in advance,and accurate long-term influenza forecasting is essential for this.Although diverse machine learning models have been proposed for influenza forecasting,they focus on short-term forecasting,and their performance is too dependent on input variables.For a country’s longterm influenza forecasting,typical surveillance data are known to be more effective than diverse external data on the Internet.We propose a two-stage data selection scheme for worldwide surveillance data to construct a longterm forecasting model for influenza in the target country.In the first stage,using a simple forecasting model based on the country’s surveillance data,we measured the change in performance by adding surveillance data from other countries,shifted by up to 52 weeks.In the second stage,for each set of surveillance data sorted by accuracy,we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage.Using the selected surveillance data,we trained a new longterm forecasting model for influenza and perform influenza forecasting for the target country.We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method.We report some of the results.