Abstract:
Lagrange Interpolation Approach" develops a mathematical model to analyze the
factors influencing tea price fluctuations in Sri Lanka. It uses Lagrange interpolation to
create a polynomial that accurately represents changes in tea prices over time, allowing
for a detailed examination of market trends based on known data points. The study
employs the Lagrange interpolation method to create a forecasting model for tea prices,
aimed at assisting estate managers, dealers, and policymakers in making informed
decisions. It highlights the movement of tea prices over time, concluding that while
natural fluctuations exist, the interpolation technique effectively captures these
variations, offering a reliable tool for predicting future price changes. In this study, we
used monthly tea production and price variation data from 2019 to 2023. We
approached in four ways to model and analyse the data in our study. They are, (i)
Monthly wise extrapolation from 2019 to 2023, from January to November and
predicted price to December of the respective year for both BOPF and BOP products,
(ii) Monthly wise interpolation from 2019 to 2023, from January to September and
November, December and predicted price to October of the respective year for both
BOPF and BOP products, (iii) Yearly wise interpolation from January to December,
from 2019, 2020, 2022 and 2023 and predicted price to each month of 2021 for both
BOPF and BOP products and (iv) Yearly wise extrapolation from January to December,
from 2019 to 2022 and predicted price to each month of 2023 for both BOPF and BOP
products. The study highlights the importance of understanding the mathematical
patterns behind agricultural commodity pricing, noting its broader implications for
various industries. By applying the Lagrange interpolation method, it demonstrates the
technique's practical value for accurate price predictions and its relevance in academic
research. This work enhances our understanding of tea pricing modeling and paves the
way for exploring more advanced strategies in agricultural economics.
