ANALISIS DINAMIKA SIKLUS PASAR PENGIRIMAN BERBASIS DATA HISTORIS DI KOTA KENDARI
Kata Kunci:
Shipping Market Cycle, Hidden Markov Model, Kendari City, VolatiAbstrak
This study aims to analyze the dynamics of the shipping market cycle in Kendari City using historical data to understand the pattern, duration, and factors that influence market fluctuations. This topic is important as the shipping market cycle has a significant impact on the performance of the shipping industry and regional economy, yet empirical studies at the local level are limited. The method used is a quantitative design with time series analysis and Hidden Markov Model (HMM) to identify market regimes and cycle transitions. Data were collected from tariff records and shipment volumes from 2008 to 2023. The results reveal the existence of market cycles with an average duration of 9 years consisting of boom, transition, and bust phases, and show the significant influence of fuel prices and the impact of the COVID-19 pandemic on market dynamics. The findings support business cycle theory and expand the understanding of shipping market volatility in the local context. The research conclusions emphasize the importance of adaptive strategies and risk management to deal with complex market fluctuations and provide a basis for policy making that supports the sustainability of the shipping industry in Kendari. The research also recommends the development of prediction models using real-time data and machine learning techniques for more accurate cycle analysis in the future.
Referensi
Alsharef, A., Aggarwal, K., Sonia, Kumar, M., & Mishra, A. (2022). Review of ML and AutoML Solutions to Forecast Time-Series Data. Archives of Computational Methods in Engineering, 29(7), 5297–5311. https://doi.org/10.1007/s11831-022-09765-0
Du, Y., Li, C., Wang, T., & Xu, Y. (2023). Special issue on “Smart port and shipping operations” in Maritime Policy & Management. Maritime Policy & Management, 50(4), 413–414. https://doi.org/10.1080/03088839.2023.2196754
Ghosh, I., & Dragan, P. (2023). Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies. Complex & Intelligent Systems, 9(4), 4169–4193. https://doi.org/10.1007/s40747-022-00947-8
Grzelakowski, A. S., Herdzik, J., & Skiba, S. (2022). Maritime Shipping Decarbonization: Roadmap to Meet Zero-Emission Target in Shipping as a Link in the Global Supply Chains.
Khan, K., Su, C. W., Khurshid, A., & Umar, M. (2022). The dynamic interaction between COVID-19 and shipping freight rates: A quantile on quantile analysis. European Transport Research Review, 14(1), 43. https://doi.org/10.1186/s12544-022-00566-x
Lv, S., Xu, Z., Fan, X., Qin, Y., & Skare, M. (2023). The mean reversion/persistence of financial cycles: Empirical evidence for 24 countries worldwide. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(1), 11–47. https://doi.org/10.24136/eq.2023.001
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2021). Machine Learning Advances for Time Series Forecasting (arXiv:2012.12802). arXiv.
https://doi.org/10.48550/arXiv.2012.12802
Notteboom, T., Pallis, T., & Rodrigue, J.-P. (2021). Disruptions and resilience in global container shipping and ports: The COVID-19 pandemic versus the 2008–2009 financial crisis. Maritime Economics & Logistics, 23(2), 179–210. https://doi.org/10.1057/s41278-020-00180-5
Oelschläger, L., Adam, T., & Michels, R. (2024). fHMM: Hidden Markov Models for Financial Time Series in R. Journal of Statistical Software, 109(9).
https://doi.org/10.18637/jss.v109.i09
Schwartz, H., Solakivi, T., & Gustafsson, M. (2022). Is There Business Potential for Sustainable Shipping? Price Premiums Needed to Cover Decarbonized Transportation.
Simon Fraser University, Jacks, D. S., Stuermer, M., & Federal Reserve Bank of Dallas. (2021). Dry Bulk Shipping and the Evolution of Maritime Transport Costs, 1850-2020. Federal Reserve Bank of Dallas, Working Papers, 2021(2102). https://doi.org/10.24149/wp2102
Tu, X., Yang, Y., Lin, Y., & Ma, S. (2023). Analysis of influencing factors and prediction of China’s Containerized Freight Index. Frontiers in Marine Science, 10, 1245542. https://doi.org/10.3389/fmars.2023.1245542
Verschuur, J., Koks, E. E., & Hall, J. W. (2021). Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data. PLOS ONE, 16(4), e0248818. https://doi.org/10.1371/journal.pone.0248818
