Forecasting of Monthly Average Wholesale Prices of Tomato: An Application of Hybrid Time Series Models

Forecasting of Monthly Average Wholesale Prices of Tomato: An Application of Hybrid Time Series Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Forecasting of Monthly Average Wholesale Prices of Tomato: An Application of Hybrid Time Series Models Pushpa Ghiyal, Joginder Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7460198/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The focus of the present study is to give short term forecast of monthly average wholesale prices of tomato using hybrid time series models. For this Hybrid models of the linear seasonal autoregressive moving average (SARIMA) and the nonlinear Artificial Neural Network (ANN) have been considered for estimating and forecasting the monthly average wholesale prices of tomato. The monthly average wholesale prices of tomato from January 2010 to December 2022 have been obtained from different markets of Haryana. The goodness of fit of SARIMA model has been measured using Akaike Information Criteria (AIC), log likelihood (LL), Root Mean Square Error (RMSE) & Mean Absolute Percentage Error (MAPE). The performance of ANN models has been measured using performance measures RMSE & MAPE. The post-sample forecast accuracy has also been measured using MAPE and standard error of prediction (SEP in %). The results of the study showed that the Hybrid (SARIMA + ANN) models have been performed better as compared to SARIMA models for forecasting the monthly average wholesale prices of tomato. The results of this study may be useful for policy makers and various stakeholders in helping them to take appropriate decisions for making arrangements for procurement, distribution, storage, and trading in local and foreign markets, as well as for managing the right inventory in advance. Earth and environmental sciences/Climate sciences Physical sciences/Mathematics and computing Price Forecasting MAPE SARIMA Hybrid Models and SEP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7460198","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531062572,"identity":"edf43b60-2834-4438-bad9-5e338a42fc51","order_by":0,"name":"Pushpa Ghiyal","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pushpa","middleName":"","lastName":"Ghiyal","suffix":""},{"id":531062573,"identity":"54f8075c-277d-414d-9be1-55bb3ce21716","order_by":1,"name":"Joginder 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