207
ISTAER2605.pdf
ISTAER2605.pdf
ISTAER2605.pdf
286
10.71451/ISTAER2605
Research on algorithm improvement of ARIMA-LSTM hybrid model in time series prediction of inflation rate
<p>As a key indicator of macroeconomic performance, inflation trends significantly influence monetary policy, macroeconomic regulation, and financial market stability. However, macroeconomic time series often contain both linear trends and complex nonlinear fluctuations, which limit the accuracy and stability of traditional statistical models. To address this, the paper proposes an improved ARIMA-LSTM hybrid forecasting model for inflation rate prediction. The ARIMA component extracts the linear structure of the series, while the residual sequence captures unexplained nonlinear information. A multi-scale LSTM network then learns deep features from the residuals, and a dynamic weight fusion mechanism adaptively combines linear and nonlinear predictions. Experiments using CPI data from IMF, World Bank, and FRED databases show that the proposed model achieves an RMSE of 0.564 on the test set—12.1% lower than the traditional ARIMA-LSTM and 17.1% lower than ARIMA alone. It also outperforms models such as SVR, random forest, LSTM, and GRU in MAE and MAPE. In multi-step forecasting, error growth remains around 12% over six steps, notably lower than comparison models. Ablation studies and Diebold–Mariano tests confirm the effectiveness of the multi-scale module and dynamic fusion mechanism. Overall, the improved ARIMA-LSTM model enhances inflation prediction accuracy and stability, offering practical value for macroeconomic forecasting and policy analysis.</p>
https://creativecommons.org/licenses/by-nc-nd/4.0
International Scientific Technical and Economic Research
2026
Inflation rate forecast; Time series analysis; ARIMA-LSTM hybrid model; Multi-scale LSTM; Dynamic weight fusion
Guona
Chen
School of Business Administration/School of Marxism, China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China
CN
cgn@st.cupk.edu.cn
https://orcid.org/0009-0002-5635-381X
219
PDF Download
0
[1] Zomchak, L., & Lapinkova, A. (2022, November). Key interest rate as a central banks tool of the monetary policy influence on inflation: the case of Ukraine. In The International Symposium on Computer Science, Digital Economy and Intelligent Systems (pp. 369-379). Cham: Springer Nature Switzerland.
[2] Cioran, Z. (2014). Monetary policy, inflation and the causal relation between the inflation rate and some of the macroeconomic variables. Procedia Economics and Finance, 16, 391-401.
[3] Priyatna, H. N., & Suryadi, I. (2025). Facing Global Inflation: Economic Strategies to Strengthen People's Purchasing Power. MSJ: Majority Science Journal, 3(1), 73-81.
[4] WANG, H., & ZHANG, X. (2022). Defusing Triple Pressure to Promote High-Quality Economic Development Rapidly. Frontiers of Economics in China, 19(1), 30.
[5] Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC medical research methodology, 21(1), 58.
[6] Lai, Y., & Dzombak, D. A. (2020). Use of the autoregressive integrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather and forecasting, 35(3), 959-976.
[7] Singh, R. K., Rani, M., Bhagavathula, A. S., Sah, R., Rodriguez-Morales, A. J., Kalita, H., ... & Kumar, P. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR public health and surveillance, 6(2), e19115.
[8] Lin, Z., Liu, Y., Wen, Z., Chen, X., Han, P., Zheng, C., ... & Shi, H. (2023). Spatial–temporal variation characteristics and driving factors of net primary production in the Yellow River Basin over multiple time scales. Remote Sensing, 15(22), 5273.
[9] Fan, D., Sun, H., Yao, J., Zhang, K., Yan, X., & Sun, Z. (2021). Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 220, 119708.
[10] Song, Y., Cai, C., Ma, D., & Li, C. (2024). Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model. Expert Systems with Applications, 237, 121527.
[11] Llerena Cana, J. P., Garcia Herrero, J., & Molina Lopez, J. M. (2021). Forecasting nonlinear systems with LSTM: analysis and comparison with EKF. Sensors, 21(5), 1805.
[12] Khan, F., Iftikhar, H., Khan, I., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). A hybrid vector autoregressive model for accurate macroeconomic forecasting: An application to the US economy. Mathematics, 13(11), 1706.
[13] Abdullah, L. T. (2022). Forecasting time series using vector autoregressive model. International Journal of Nonlinear Analysis and Applications, 13(1), 499-511.
[14] Sako, K., Mpinda, B. N., & Rodrigues, P. C. (2022). Neural networks for financial time series forecasting. Entropy, 24(5), 657.
[15] Fang, Z., Ma, X., Pan, H., Yang, G., & Arce, G. R. (2023). Movement forecasting of financial time series based on adaptive LSTM-BN network. Expert Systems with Applications, 213, 119207.
[16] Lazcano, A., Herrera, P. J., & Monge, M. (2023). A combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting. Mathematics, 11(1), 224.
[17] Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia exports using a hybrid model ARIMA-LSTM. Procedia Computer Science, 179, 480-487.
[18] Jin, Y. C., Cao, Q., Wang, K. N., Zhou, Y., Cao, Y. P., & Wang, X. Y. (2023). Prediction of COVID-19 data using improved ARIMA-LSTM hybrid forecast models. IEEE Access, 11, 67956-67967.
[19] Desalegn, G., Tangl, A., & Fekete-Farkas, M. (2022). From short-term risk to long-term strategic challenges: Reviewing the consequences of geopolitics and COVID-19 on economic performance. Sustainability, 14(21), 14455.
[20] Derkenbaeva, S., Galushkina, E., Soodonbekova, A., Beksultanov, A., & Kozubekova, S. (2025). Impact of global economic instability on social policies: Adaptation and resilience strategies. Social Sciences & Humanities Open, 12, 101946.
[21] Han, Z., Zhao, J., Leung, H., Ma, K. F., & Wang, W. (2019). A review of deep learning models for time series prediction. IEEE Sensors Journal, 21(6), 7833-7848.
[22] Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Muhammad Marier, S., ... & Xia, F. (2025). Deep learning for time series forecasting: a survey. International Journal of Machine Learning and Cybernetics, 16(7), 5079-5112.
[23] Ernest, K., Theophilus, A. K., Amoah, P., & Emmanuel, B. B. (2019). Identifying key economic indicators influencing tender price index prediction in the building industry: a case study of Ghana. International journal of construction management, 19(2), 106-112.
[24] Adarov, A. (2021). Dynamic interactions between financial cycles, business cycles and macroeconomic imbalances: A panel VAR analysis. International Review of Economics & Finance, 74, 434-451.
[25] Spence, C. (2021). Explaining seasonal patterns of food consumption. International journal of gastronomy and food science, 24, 100332.
[26] Batten, S., Sowerbutts, R., & Tanaka, M. (2020). Climate change: Macroeconomic impact and implications for monetary policy. Ecological, societal, and technological risks and the financial sector, 13-38.
[27] Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023). Deep learning for time series forecasting: Advances and open problems. Information, 14(11), 598.
[28] Ding, Y., & Ding, G. (2026). Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network. International Scientific Technical and Economic Research,4(1),70-89
[29] Zhao, T., Chen, G., Gatewongsa, T., & Busababodhin, P. (2025). Forecasting Agricultural Trade Based on TCN-LightGBM Models: A Data-Driven Decision. Research on World Agricultural Economy, 207-221.
[30] Zeng, X. (2026). Cross-Border Trade Fraud Detection via Integrated Heterogeneous Graph Neural Network and XGBoost. International Scientific Technical and Economic Research,4(1),47-69
[31] Du, Y., Chen, G., Pang, C., & Zhao, T. (2026). Prediction of fracture and vug parameters in carbonate reservoirs using a combined T-GNO-PINN approach. Journal of Seismic Exploration, 35(1), 46–65.
[32] Wang, H. (2026). Supply Chain Digital Integration and Operational Resilience: An Empirical Study Based on Matched Data Between Manufacturing and Logistics Firms in Central and Eastern Europe. International Scientific Technical and Economic Research,4(1),23-46
[33] Zhao, T., Chen, G., Suraphee, S., Phoophiwfa, T., & Busababodhin, P. (2025). A hybrid TCN-XGBoost model for agricultural product market price forecasting. PLoS One, 20(5), e0322496.
[34] Wang, W., Shen, S., & Wang, Y. (2026). Forecasting Short-Term Export Volumes with Hybrid Models Integrating SARIMA with Attention-Based LSTM. International Scientific Technical and Economic Research,4(1),1-22
[35] Chen, G., Zhao, T., Pang, C., Seenoi, P., Papukdee, N., & Busababodhin, P. (2025). An attention-guided graph neural network and U-Net++-based reservoir porosity prediction system. Journal of Seismic Exploration, 34(4), 70-87.
[36] Zhao, T., Chen, G., Pang, C., & Busababodhin, P. (2025). Application and Performance Optimization of SLHS-TCN-XGBoost Model in Power Demand Forecasting. CMES-Computer Modeling in Engineering and Sciences, 143(3), 2883-2917.
[37] Zhao, L., Wen, X., Wang, Y., & Shao, Y. (2022). A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction. IET Intelligent Transport Systems, 16(2), 206-217.
4
1
2026
Volume. 4, No. 1 (March 2026)
90-122
287
10.71451/ISTAER2605
Research on Algorithm Improvement of ARIMA-LSTM Hybrid Model in Time Series Prediction of Inflation Rate
<p>As a key indicator of macroeconomic performance, inflation trends significantly influence monetary policy, macroeconomic regulation, and financial market stability. However, macroeconomic time series often contain both linear trends and complex nonlinear fluctuations, which limit the accuracy and stability of traditional statistical models. To address this, the paper proposes an improved ARIMA-LSTM hybrid forecasting model for inflation rate prediction. The ARIMA component extracts the linear structure of the series, while the residual sequence captures unexplained nonlinear information. A multi-scale LSTM network then learns deep features from the residuals, and a dynamic weight fusion mechanism adaptively combines linear and nonlinear predictions. Experiments using CPI data from IMF, World Bank, and FRED databases show that the proposed model achieves an RMSE of 0.564 on the test set—12.1% lower than the traditional ARIMA-LSTM and 17.1% lower than ARIMA alone. It also outperforms models such as SVR, random forest, LSTM, and GRU in MAE and MAPE. In multi-step forecasting, error growth remains around 12% over six steps, notably lower than comparison models. Ablation studies and Diebold–Mariano tests confirm the effectiveness of the multi-scale module and dynamic fusion mechanism. Overall, the improved ARIMA-LSTM model enhances inflation prediction accuracy and stability, offering practical value for macroeconomic forecasting and policy analysis.</p>
https://creativecommons.org/licenses/by-nc-nd/4.0
International Scientific Technical and Economic Research
2026
Inflation rate forecast; Time series analysis; ARIMA-LSTM hybrid model; Multi-scale LSTM; Dynamic weight fusion
Guona
Chen
School of Business Administration/School of Marxism, China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China
CN
cgn@st.cupk.edu.cn
https://orcid.org/0009-0002-5635-381X
220
PDF Download
0
[1] Zomchak, L., & Lapinkova, A. (2022, November). Key interest rate as a central banks tool of the monetary policy influence on inflation: the case of Ukraine. In The International Symposium on Computer Science, Digital Economy and Intelligent Systems (pp. 369-379). Cham: Springer Nature Switzerland.
[2] Cioran, Z. (2014). Monetary policy, inflation and the causal relation between the inflation rate and some of the macroeconomic variables. Procedia Economics and Finance, 16, 391-401.
[3] Priyatna, H. N., & Suryadi, I. (2025). Facing Global Inflation: Economic Strategies to Strengthen People's Purchasing Power. MSJ: Majority Science Journal, 3(1), 73-81.
[4] WANG, H., & ZHANG, X. (2022). Defusing Triple Pressure to Promote High-Quality Economic Development Rapidly. Frontiers of Economics in China, 19(1), 30.
[5] Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC medical research methodology, 21(1), 58.
[6] Lai, Y., & Dzombak, D. A. (2020). Use of the autoregressive integrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather and forecasting, 35(3), 959-976.
[7] Singh, R. K., Rani, M., Bhagavathula, A. S., Sah, R., Rodriguez-Morales, A. J., Kalita, H., ... & Kumar, P. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (ARIMA) model. JMIR public health and surveillance, 6(2), e19115.
[8] Lin, Z., Liu, Y., Wen, Z., Chen, X., Han, P., Zheng, C., ... & Shi, H. (2023). Spatial–temporal variation characteristics and driving factors of net primary production in the Yellow River Basin over multiple time scales. Remote Sensing, 15(22), 5273.
[9] Fan, D., Sun, H., Yao, J., Zhang, K., Yan, X., & Sun, Z. (2021). Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 220, 119708.
[10] Song, Y., Cai, C., Ma, D., & Li, C. (2024). Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model. Expert Systems with Applications, 237, 121527.
[11] Llerena Cana, J. P., Garcia Herrero, J., & Molina Lopez, J. M. (2021). Forecasting nonlinear systems with LSTM: analysis and comparison with EKF. Sensors, 21(5), 1805.
[12] Khan, F., Iftikhar, H., Khan, I., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). A hybrid vector autoregressive model for accurate macroeconomic forecasting: An application to the US economy. Mathematics, 13(11), 1706.
[13] Abdullah, L. T. (2022). Forecasting time series using vector autoregressive model. International Journal of Nonlinear Analysis and Applications, 13(1), 499-511.
[14] Sako, K., Mpinda, B. N., & Rodrigues, P. C. (2022). Neural networks for financial time series forecasting. Entropy, 24(5), 657.
[15] Fang, Z., Ma, X., Pan, H., Yang, G., & Arce, G. R. (2023). Movement forecasting of financial time series based on adaptive LSTM-BN network. Expert Systems with Applications, 213, 119207.
[16] Lazcano, A., Herrera, P. J., & Monge, M. (2023). A combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting. Mathematics, 11(1), 224.
[17] Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia exports using a hybrid model ARIMA-LSTM. Procedia Computer Science, 179, 480-487.
[18] Jin, Y. C., Cao, Q., Wang, K. N., Zhou, Y., Cao, Y. P., & Wang, X. Y. (2023). Prediction of COVID-19 data using improved ARIMA-LSTM hybrid forecast models. IEEE Access, 11, 67956-67967.
[19] Desalegn, G., Tangl, A., & Fekete-Farkas, M. (2022). From short-term risk to long-term strategic challenges: Reviewing the consequences of geopolitics and COVID-19 on economic performance. Sustainability, 14(21), 14455.
[20] Derkenbaeva, S., Galushkina, E., Soodonbekova, A., Beksultanov, A., & Kozubekova, S. (2025). Impact of global economic instability on social policies: Adaptation and resilience strategies. Social Sciences & Humanities Open, 12, 101946.
[21] Han, Z., Zhao, J., Leung, H., Ma, K. F., & Wang, W. (2019). A review of deep learning models for time series prediction. IEEE Sensors Journal, 21(6), 7833-7848.
[22] Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Muhammad Marier, S., ... & Xia, F. (2025). Deep learning for time series forecasting: a survey. International Journal of Machine Learning and Cybernetics, 16(7), 5079-5112.
[23] Ernest, K., Theophilus, A. K., Amoah, P., & Emmanuel, B. B. (2019). Identifying key economic indicators influencing tender price index prediction in the building industry: a case study of Ghana. International journal of construction management, 19(2), 106-112.
[24] Adarov, A. (2021). Dynamic interactions between financial cycles, business cycles and macroeconomic imbalances: A panel VAR analysis. International Review of Economics & Finance, 74, 434-451.
[25] Spence, C. (2021). Explaining seasonal patterns of food consumption. International journal of gastronomy and food science, 24, 100332.
[26] Batten, S., Sowerbutts, R., & Tanaka, M. (2020). Climate change: Macroeconomic impact and implications for monetary policy. Ecological, societal, and technological risks and the financial sector, 13-38.
[27] Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023). Deep learning for time series forecasting: Advances and open problems. Information, 14(11), 598.
[28] Ding, Y., & Ding, G. (2026). Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network. International Scientific Technical and Economic Research,4(1),70-89.
[29] Zhao, T., Chen, G., Gatewongsa, T., & Busababodhin, P. (2025). Forecasting Agricultural Trade Based on TCN-LightGBM Models: A Data-Driven Decision. Research on World Agricultural Economy, 207-221.
[30] Zeng, X. (2026). Cross-Border Trade Fraud Detection via Integrated Heterogeneous Graph Neural Network and XGBoost. International Scientific Technical and Economic Research,4(1),47-69.
[31] Du, Y., Chen, G., Pang, C., & Zhao, T. (2026). Prediction of fracture and vug parameters in carbonate reservoirs using a combined T-GNO-PINN approach. Journal of Seismic Exploration, 35(1), 46–65.
[32] Wang, H. (2026). Supply Chain Digital Integration and Operational Resilience: An Empirical Study Based on Matched Data Between Manufacturing and Logistics Firms in Central and Eastern Europe. International Scientific Technical and Economic Research,4(1),23-46.
[33] Zhao, T., Chen, G., Suraphee, S., Phoophiwfa, T., & Busababodhin, P. (2025). A hybrid TCN-XGBoost model for agricultural product market price forecasting. PLoS One, 20(5), e0322496.
[34] Wang, W., Shen, S., & Wang, Y. (2026). Forecasting Short-Term Export Volumes with Hybrid Models Integrating SARIMA with Attention-Based LSTM. International Scientific Technical and Economic Research,4(1),1-22.
[35] Chen, G., Zhao, T., Pang, C., Seenoi, P., Papukdee, N., & Busababodhin, P. (2025). An attention-guided graph neural network and U-Net++-based reservoir porosity prediction system. Journal of Seismic Exploration, 34(4), 70-87.
[36] Zhao, T., Chen, G., Pang, C., & Busababodhin, P. (2025). Application and Performance Optimization of SLHS-TCN-XGBoost Model in Power Demand Forecasting. CMES-Computer Modeling in Engineering and Sciences, 143(3), 2883-2917.
[37] Zhao, L., Wen, X., Wang, Y., & Shao, Y. (2022). A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction. IET Intelligent Transport Systems, 16(2), 206-217.
4
1
2026
Volume. 4, No. 1 (March 2026)
90-122