PulseTrendingJournal ClubResearchersJournalsExplore
Instagram
HomeTrendingJournal ClubExplore
Synapse
⌘+K
Synapse
April 8, 2026Scientific ReportsOpen Access

Forecasting tomato production in major Asian producers: a comparative study of ARIMA, exponential smoothing, score-driven models, and XGBoost

View Full Paper
Ask AI
Bookmark
Share

Authors

AKAbdullah Mohammad Ghazi Al khatibBABayan Mohamad AlshaibPMPradeep Mishra

Discussion

Loading...

Member takes

Overview

Comparative study forecasts tomato production in major Asian producers, suggesting future trends for resource planning.

Key Points

  • The study investigates patterns and forecasts of tomato production in five major Asian countries using advanced models.
  • Utilized a time series dataset from 1961 to 2021, divided into training (1961-2014) and validation (2015-2021) periods.
  • Applied ARIMA, Exponential Smoothing, Score-Driven models, and XGBoost for forecasting.
  • Evaluated model performance through information criteria, error metrics, and diagnostic tests.
  • XGBoost produced the lowest validation errors for several countries, reflecting recent volatility.
  • Exponential Smoothing was deemed optimal for forecasting Bangladesh's production.
  • Score-Driven models excelled in performance for China, India, Pakistan, and Sri Lanka.
  • Forecasts through 2028 indicate upward trends for Bangladesh, China, India, and Pakistan, with stabilization for Sri Lanka.

Cite This Study

khatib et al. (2026) studied this question.

synapsesocial.com/papers/69d5f11e74eaea4b11a7aa5bhttps://doi.org/10.1038/s41598-026-46110-y
View Full Paper
Ask AI
Bookmark
Share