Chinas Urban and Rural CPI Prediction Based on ARIMA Model DOI Creative Commons

Liu Yu-zhi

Advances in Economics Management and Political Sciences, Год журнала: 2023, Номер 50(1), С. 91 - 98

Опубликована: Ноя. 30, 2023

Inflation represents the continuous rise of overall price level a country. In severe cases, it may cause an imbalance between social supply and demand lead to crisis currency confidence. Therefore, is necessary measure predict inflation. The CPI index important indicator inflation, which can largely reflect national economic situation in certain period. This paper conducts research by selecting urban rural data National Bureau Statistics from January 2007 June 2023, total 198 months. After processing inspection, this use ARIMA model forecast. experimental results show that (12,0,1) (12,0,0) have good predictive effects on cities villages respectively. short term, accurately changing trend index, with error rate less than 0.5%. predicts China's inflation 2023 2024 will be stable improving overall.

Язык: Английский

Analysis & forecasting of juvenile crime using variance threshold and time series algorithm DOI
Harshita Jain, Ravindra Patel

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июль 29, 2024

Язык: Английский

Процитировано

1
Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model DOI Creative Commons
Tanattrin Bunnag

International Journal of Energy Economics and Policy, Год журнала: 2024, Номер 14(1), С. 418 - 426

Опубликована: Янв. 15, 2024

This paper analyzes the relationship between air pollutants and amount of PM10 measured in Bangkok. It forecasts Bangkok by using SARIMA SARIMA-GARCH models to formulate policies reduce occurrence guidelines for further prevention. PM's data is from January 2008 July 2023. First, process build Model Estimation. We perform model comparisons that (3,1,3)(1,1,2)12 SARIMA(3,1,3)(1,1,2)12-GARCH(1,1), which gives lower MAE RMSE values, indicates good prediction accuracy than another model. The results show predictions (3,1,3) (1,1,2)12 are 15.303 20.839 better those (1,1,2)12-GARCH (1,1) 17.280 22.677. Therefore, forecast precise. Thus, summary, we will choose first use forecasting policy making. Moreover, study results, found elements NO2 O3 require quite a lot attention because they affect with at moderate level.

Язык: Английский

Процитировано

1
Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model DOI
Jatinder Kaur, Sarbjit Singh, Kulwinder Singh Parmar

и другие.

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(45), С. 101035 - 101052

Опубликована: Авг. 29, 2023

Язык: Английский

Процитировано

3
Empirical assessment of transformer-based neural network architecture in forecasting pollution trends DOI
Pritthijit Nath, Asif Iqbal Middya, Sarbani Roy

и другие.

International Journal of Data Science and Analytics, Год журнала: 2023, Номер unknown

Опубликована: Июнь 30, 2023

Язык: Английский

Процитировано

2
Chinas Urban and Rural CPI Prediction Based on ARIMA Model DOI Creative Commons

Liu Yu-zhi

Advances in Economics Management and Political Sciences, Год журнала: 2023, Номер 50(1), С. 91 - 98

Опубликована: Ноя. 30, 2023

Inflation represents the continuous rise of overall price level a country. In severe cases, it may cause an imbalance between social supply and demand lead to crisis currency confidence. Therefore, is necessary measure predict inflation. The CPI index important indicator inflation, which can largely reflect national economic situation in certain period. This paper conducts research by selecting urban rural data National Bureau Statistics from January 2007 June 2023, total 198 months. After processing inspection, this use ARIMA model forecast. experimental results show that (12,0,1) (12,0,0) have good predictive effects on cities villages respectively. short term, accurately changing trend index, with error rate less than 0.5%. predicts China's inflation 2023 2024 will be stable improving overall.

Язык: Английский

Процитировано

1