IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2604.01792.html

Quantifying Inter-Annual Seasonal Drift in Tomato Prices Using Dynamic Time Warping: Evidence from Kolar Market

Author

Listed:
  • Manojkumar Patil
  • Lalith Achoth
  • K. B. Vedamurthy
  • K. B. Umesh
  • Siddayya
  • M. N. Thimme Gowda

Abstract

Tomato prices in Kolar market exhibit high volatility alongside recurring seasonal patterns, but the consistency of these patterns across years remains unclear. This study analysed weekly tomato prices and arrivals from 2010-2024 to quantify inter-annual variability using descriptive statistics, seasonal indices, and Dynamic Time Warping (DTW). Descriptive analysis confirmed extreme fluctuations (CV = 77% for prices, 102% for arrivals) with positive skewness and heavy tails, indicating frequent extreme events. Seasonal indices revealed recurring intra-year cycles, but year-to-year alignment varied substantially. DTW analysis for 2021-2024 quantified pattern similarity, showing that 2022-2023 had the highest alignment (DTW distance: 23,258) despite extreme price spikes, whereas 2021-2022 exhibited the weakest alignment (distance: 39,049), reflecting structural shifts in market dynamics. Path length metrics indicated minimal temporal warping in 2022-2023 (71 points) versus extensive alignment in 2021-2022 (83 points). These results demonstrate that while seasonal patterns recur, their temporal consistency is not fixed, highlighting the need for forecasting models that adapt to both magnitude volatility and temporal shifts. The study also illustrates the utility of DTW for agricultural price analysis and the limitations of relying solely on fixed seasonal patterns in volatile commodity markets.

Suggested Citation

  • Manojkumar Patil & Lalith Achoth & K. B. Vedamurthy & K. B. Umesh & Siddayya & M. N. Thimme Gowda, 2026. "Quantifying Inter-Annual Seasonal Drift in Tomato Prices Using Dynamic Time Warping: Evidence from Kolar Market," Papers 2604.01792, arXiv.org.
    • Handle: RePEc:arx:papers:2604.01792
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2604.01792
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mittal, Surabhi, 2007. "Strengthening Backward and Forward Linkages in Horticulture: Some Successful Initiatives," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 20(Conferenc).
    2. Christopher Gilbert & Wyn Morgan, 2010. "Has food price volatility risen?," Department of Economics Working Papers 1002, Department of Economics, University of Trento, Italia.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    5. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    6. Miljkovic, Dragan & Vatsa, Puneet, 2023. "On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wong, Jian Cheng & Lian, Heng & Cheong, Siew Ann, 2009. "Detecting macroeconomic phases in the Dow Jones Industrial Average time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(21), pages 4635-4645.
    2. Krishnamurthy, Vikram & Leoff, Elisabeth & Sass, Jörn, 2018. "Filterbased stochastic volatility in continuous-time hidden Markov models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 1-21.
    3. Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
    4. Kai Zheng & Weidong Xu & Xili Zhang, 2023. "Multivariate Regime Switching Model Estimation and Asset Allocation," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 165-196, January.
    5. Blanka Horvath & Zacharia Issa & Aitor Muguruza, 2021. "Clustering Market Regimes using the Wasserstein Distance," Papers 2110.11848, arXiv.org.
    6. Salam Rabindrajit Luwang & Buddha Nath Sharma & Kundan Mukhia & Md. Nurujjaman & Anish Rai & Filippo Petroni & Luis E. C. Rocha, 2026. "Regime Discovery and Intra-Regime Return Dynamics in Global Equity Markets," Papers 2601.08571, arXiv.org.
    7. Aliyu, Shehu Usman Rano, 2020. "What have we learnt from modelling stock returns in Nigeria: Higgledy-piggledy?," MPRA Paper 110382, University Library of Munich, Germany, revised 06 Jun 2021.
    8. Göncü, Ahmet & Karahan, Mehmet Oğuz & Kuzubaş, Tolga Umut, 2016. "A comparative goodness-of-fit analysis of distributions of some Lévy processes and Heston model to stock index returns," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 69-83.
    9. Eric Vansteenberghe, 2026. "Quantitative Methods in Finance," Papers 2601.12896, arXiv.org, revised Mar 2026.
    10. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    11. Focardi, Sergio M. & Fabozzi, Frank J. & Mazza, Davide, 2019. "Modeling local trends with regime shifting models with time-varying probabilities," International Review of Financial Analysis, Elsevier, vol. 66(C).
    12. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2022. "On the volatility of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 62(C).
    13. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.
    14. Stijn De Backer & Luis E. C. Rocha & Jan Ryckebusch & Koen Schoors, 2026. "Characterizing asymmetric and bimodal long-term financial return distributions through quantum walks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 99(4), pages 1-20, April.
    15. Kai Zheng & Yuying Li & Weidong Xu, 2021. "Regime switching model estimation: spectral clustering hidden Markov model," Annals of Operations Research, Springer, vol. 303(1), pages 297-319, August.
    16. Vikram Krishnamurthy & Elisabeth Leoff & Jorn Sass, 2016. "Filterbased Stochastic Volatility in Continuous-Time Hidden Markov Models," Papers 1602.05323, arXiv.org.
    17. Onur Enginar & Kazim Baris Atici, 2022. "Optimal forecast error as an unbiased estimator of abnormal return: A proposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 158-166, January.
    18. Hsu, Pao-Peng, 2017. "Examination of Taiwan's travel and tourism market cycle through a two-period Markov regime-switching model," Tourism Management, Elsevier, vol. 63(C), pages 201-208.
    19. BBVA Research & Alvaro Ortiz & Tomasa Rodrigo, 2025. "Global | Geopolítica, geoeconomía y riesgo: un enfoque basado en aprendizaje automático [Global | Geopolitics, geoeconomics and risk: a machine learning approach]," Working Papers 25/14, BBVA Bank, Economic Research Department.
    20. El Khatib, Youssef & Goutte, Stéphane & Ma, Feng & Vigne, Samuel A., 2025. "Impact of exogenous events on volatility derivatives pricing," Research in International Business and Finance, Elsevier, vol. 80(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2604.01792. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.