id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fbem-25626	Liu, Yu	Forecasting China's Consumer Price Index (CPI) Based on Combined ARIMA-LSTM Models	2024	7	.pdf	application/pdf	5076	282	55	CPI price indices tend to have certain seasonality and trends, and ARIMA models can capture these linear relationships, providing forecasting power for long-term trends (Riofrio et al., 2023; Álvarez-Díaz & Gupta, 2016). In addition, there are various improved and extended forms of ARIMA models, such as the Seasonal ARIMA (SARIMA) model, which can better handle time series data with specific properties.	cache/fbem-25626.pdf	txt/fbem-25626.txt