Quantifying Inter-Annual Seasonal Drift in Tomato Prices Using Dynamic Time Warping: Evidence from Kolar Market
Manojkumar Patil, Lalith Achoth, K. B. Vedamurthy, K. B. Umesh, Siddayya, M. N. Thimme Gowda

TL;DR
This study uses Dynamic Time Warping to analyze inter-annual variability in tomato prices in Kolar, revealing that seasonal patterns recur but vary significantly across years, impacting forecasting models.
Contribution
It introduces DTW as a tool to quantify temporal shifts in seasonal price patterns, highlighting the need for adaptive forecasting in volatile markets.
Findings
2022-2023 had the highest pattern alignment despite price spikes
2021-2022 showed the weakest alignment, indicating structural shifts
Seasonal patterns recur but with significant temporal variability
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.…
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