  Time-series data analysis is important because numerous real-world tasks such
as forecasting weather, electricity consumption, and stock market involve
predicting data that vary over time. Time-series data are generally recorded
over a long period of observation with long sequences owing to their periodic
characteristics and long-range dependencies over time. Thus, capturing
long-range dependency is an important factor in time-series data forecasting.
To solve these problems, we proposed two novel modules, Grouped Self-Attention
(GSA) and Compressed Cross-Attention (CCA). With both modules, we achieved a
computational space and time complexity of order $O(l)$ with a sequence length
$l$ under small hyperparameter limitations, and can capture locality while
considering global information. The results of experiments conducted on
time-series datasets show that our proposed model efficiently exhibited reduced
computational complexity and performance comparable to or better than existing
methods.
