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2020•Unpublished venueRequires access

Research on CPI Prediction Based on Space-Time Model

Songyan Ji, Jian Dong, Ye Wang, Yanxin Liu

Open publisher page 4 citations

Abstract

Consumer price index (CPI) prediction is an effective approach to measure inflation and provide a reference to formulate economic development strategy. The Autoregressive Integrated Moving Average (ARIMA) model is a classic model to predict CPI. However, a main drawback of ARIMA model is that it only utilizes the time effect while ignoring inter-regional economic interaction which is another significant effect on CPI. Aiming at this, the Generalized Space Time Autoregressive Integrated (GSTARI) model is proposed. In this paper, we verify and compare the prediction accuracy of both GSTARI model and classic ARIMA model with the CPI data of 4 main cities (Dalian, Shenyang, Changchun and Harbin) in China. Our experiments show that for most of cities, GSTARI model have 7%-38% higher prediction accuracy than ARIMA model.

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What this paper is about

Consumer price index (CPI) prediction is an effective approach to measure inflation and provide a reference to formulate economic development strategy. The Autoregressive Integrated Moving Average (ARIMA) model is a classic model to predict CPI. However, a main drawback of ARIMA model is that it only utilizes the time effect while ignoring inter-regional economic interaction which is another significant effect on CPI. Aiming at this, the Generalized Space Time Autoregressive Integrated (GSTARI) model is proposed. In this paper, we verify and compare the prediction accuracy of both GSTARI model and classic ARIMA model with the CPI data of 4 main cities (Dalian, Shenyang, Changchun and Harbin) in China. Our experiments show that for most of cities, GSTARI model have 7%-38% higher prediction accuracy than ARIMA model.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Consumer price index (CPI) prediction is an effective approach to measure inflation and provide a reference to formulate economic development strategy. The Autoregressive Integrated Moving Average (ARIMA) model is a classic model to predict CPI. However, a main drawback of ARIMA model is that it only utilizes the time effect while ignoring inter-regional economic interaction which is another significant effect on CPI. Aiming at this, the Generalized Space Time Autoregressive Integrated (GSTARI) model is proposed. In this paper, we verify and compare the prediction accuracy of both GSTARI model and classic ARIMA model with the CPI data of 4 main cities (Dalian, Shenyang, Changchun and Harbin) in China. Our experiments show that for most of cities, GSTARI model have 7%-38% higher prediction accuracy than ARIMA model.

Key concepts: Autoregressive integrated moving average, Autoregressive model, Inflation (cosmology), Econometrics, Computer science, Time series, Index (typography), Economics

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