Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE ≤ ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs.

• Adam, A. I. (2022). Time series analysis of rice prices using Box-Jenkins ARIMA methodology: Case study Hargeisa-Somaliland. Daha International University Academic Journal, 1(1), 22-34. https://doi.org/10.5281/zenodo.7193147

• Areef, M., & Radha, Y. (2020). Application of GARCH and ANN models for potato price forecasting: A case study of Bangalore market, Karnataka state. Indian Journal of Agricultural Marketing, 34(3), 44-52.

• Asnhari, S. F., Gunawan, P. H., & Rusmawati, Y. (2019). Predicting staple food materials price using multivariables factors (regression and Fourier models with ARIMA). In 2019 7th International Conference on Information and Communication Technology (ICoICT) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/ICoICT.2019.8835193

• Astiningrum, M., Wijayaningrum, V. N., & Putri, I. K. (2021). Analysis of staple food price forecasting results using various approaches. In 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 625-630). IEEE Publishing. https://doi.org/10.1109/ICEEIE52663.2021.9616763

• Brownlee, J. (2020). How to develop LSTM models for time series forecasting. Guiding Tech Media. https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/

• Chakraborty, A., Das, D., Mitra, S., De, D., & Pal, A. J. (2022). Forecasting adversities of COVID-19 waves in India using intelligent computing. Innovations in Systems and Software Engineering, 19(3), 1-17. https://doi.org/10.1007/s11334-022-00486-y

• Choong, K. Y., Raof, R. A. A., Sudin, S., & Ong, R. J. (2021). Time series analysis for vegetable price forecasting in e-commerce platform: A review. Journal of Physics: Conference Series, 1878, Article 012071. https://doi.org/10.1088/1742-6596/1878/1/012071

• Choudhury, K., Jha, G. K., Das, P., & Chaturvedi, K. K. (2019). Forecasting potato price using ensemble artificial neural networks. Indian Journal of Extension Education, 55(1), 73-77.

• Dacha, K., Cherukupalli, R., & Sinha, A. (2021). Food index forecasting. In Applied Advanced Analytics: 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence (pp. 125-134). Springer. https://doi.org/10.1007/978-981-33-6656-5_11

• Das, D., & Chakrabarti, S. (2021). Forecast model development of some selected wholesale price index of India using MLP. In Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing (pp. 217-230). Springer. https://doi.org/10.1007/978-981-33-4968-1_18

• Dewi, N. P., & Listiowarni, I. (2020). Implementasi Holt-Winters exponential smoothing untuk peramalan harga bahan pangan di kabupaten pamekasan [Implementation of Holt-Winters exponential smoothing for forecasting food prices in Pamekasan Regency]. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 11(2), 219-231.

• Erdem, K. (2020). Introduction to extreme learning machines. Towards Data Science.

• Fan, R. Y., Ng, S. T., & Wong, J. M. (2010). Reliability of the Box–Jenkins model for forecasting construction demand covering times of economic austerity. Construction Management and Economics, 28(3), 241-254. https://doi.org/10.1080/01446190903369899

• Feng, Z. K., Niu, W. J., Tang, Z. Y., Xu, Y., & Zhang, H. R. (2021). Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction. Journal of Hydrology, 595, Article 126062. https://doi.org/10.1016/j.jhydrol.2021.126062

• Fernando, W. H. H., Jayalath, P. M. S. C., Premarathne, R. M. S. M., & Chandrasekara, N. V. (2021, February 17). Time series model to forecast monthly average white raw rice prices in Colombo, Sri Lanka [Paper Presented]. Proceedings of 8th Ruhuna International Science and Technology Conference, Matara, Sri Lanka.

• Fitria, V. A. (2018). Parameter optimization of single exponential smoothing using golden section method for groceries forecasting. Jurnal Sains, Matematika, dan Terapan, 4(2), 89-97.

• Frias-Bustamante, M.P., Martinez-Rodriguez, A.M., Conde-Sanchez, A., & Martinez, F. (2022). tsfgrnn: Time Series Forecasting Using GRNN [R package version 1.0.4]. https://cran.rstudio.com/web/packages/tsfgrnn/index.html

• Gaspar, I., Castro, R., & Sousa, T. (2021). Optimisation and economic feasibility of battery energy storage systems in electricity markets: The Iberian market case study. Journal of Cleaner Production, 324, Article 129255. https://doi.org/10.1016/j.jclepro.2021.129255

• Ge, Y., & Wu, H. (2020). Prediction of corn price fluctuation based on multiple linear regression analysis model under big data. Neural Computing and Applications, 32(22), 16843-16855. https://doi.org/10.1007/s00521-018-03970-4

• Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of statistical software, 27, 1-22. https://doi.org/10.18637/jss.v027.i03

• Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2022). forecast: Forecasting functions for time series and linear models [R package version 8.21.1]. https://cran.r-project.org/web/packages/forecast/index.html

• Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., & O’Hara-Wild, M. (2022). tsfeatures: Time Series Feature Extraction [R package version 1.1.1]. https://cran.r-project.org/web/packages/tsfeatures/index.html

• Jia, M., Huang, J., Pang, L., & Zhao, Q. (2019). Analysis and research on stock price of LSTM and bidirectional LSTM neural network. In 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019) (pp. 467-473). Atlantis Press.

• Koblianska, I., Kalachevska, L., Minta, S., Strochenko, N., & Lukash, S. (2021). Modelling and forecasting of potato sales prices in Ukraine. Agricultural and Resource Economics: International Scientific E-Journal, 7(4), 160-179. https://doi.org/10.22004/ag.econ.316826

• Kourentzes, N. (2019a). nnfor: Time series forecasting with neural networks [R package version 0.9.8]. https://cran.r-project.org/web/packages/nnfor/index.html

• Kourentzes, N. (2019b). Tutorial for the nnfor R package. https://kourentzes.com/forecasting/2019/01/16/tutorial-for-the-nnfor-r-package/

• Lutfi, M., Muttaqien, H., Apriliani, A., Zainuddin, H., & Yuyun, Y. (2019). Application of the Naïve bayes algorithm and simple exponential smoothing for food commodity prices forecasting. In 1st International Conference on Science and Technology, ICOST (pp. 1-7). EAI. http://dx.doi.org/10.4108/eai.2-5-2019.2284613

• MacLachlan, M. J., Chelius, C. A., & Short, G. (2022). Time-series methods for forecasting and modeling uncertainty in the food price outlook (Technical Bulletin Number - 1957). Economic Research Service, U.S. Department of Agriculture.

• Mahto, A. K., Alam, M. A., Biswas, R., Ahmed, J., & Alam, S. I. (2021). Short-term forecasting of agriculture commodities in context of Indian market for sustainable agriculture by using the artificial neural network. Journal of Food Quality, 2021, Article 9939906. https://doi.org/10.1155/2021/9939906

• Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing prophet and deep learning to ARIMA in forecasting wholesale food prices. Forecasting, 3(3), 644-662. https://doi.org/10.3390/forecast3030040

• Mgale, Y. J., Yan, Y., & Timothy, S. (2021). A comparative study of ARIMA and Holt-Winters exponential smoothing models for rice price forecasting in Tanzania. Open Access Library Journal, 8(5), 1-9. https://doi.org/10.4236/oalib.1107381

• Mishra, M. K., Sisodia, B. V. S., & Rai, V. N. (2019). Forecasting technique of price of potato of Uttar Pradesh. Journal of Pharmacognosy and Phytochemistry, 8(3), 60-62.

• Niu, W. J., Feng, Z. K., Zeng, M., Feng, B. F., Min, Y. W., Cheng, C. T., & Zhou, J. Z. (2019). Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Applied Soft Computing, 82, Article 105589. https://doi.org/10.1016/j.asoc.2019.105589

• Patel, J., Patel, M., & Darji, M. (2018). Stock price prediction using RNN and LSTM. Journal of Emerging Technologies and Innovative Research, 5(11), 1069-1079.

• Perone, G. (2022). Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. The European Journal of Health Economics, 23(6), 917-940. https://doi.org/10.1007/s10198-021-01347-4

• Prakash, P., Jaganathan, D., Immanuel, S., Lama, A., Sreekumar, J., & Sivakumar, P. S. (2022). Forecasting of sweet potato (Ipomoea batatas L.) prices in India. Indian Journal of Extension Education, 58(2), 15-20. https://doi.org/10.48165/IJEE.2022.58203

• Purohit, S. K., Panigrahi, S., Sethy, P. K., & Behera, S. K. (2021). Time series forecasting of price of agricultural products using hybrid methods. Applied Artificial Intelligence, 35(15), 1388-1406. https://doi.org/10.1080/08839514.2021.1981659

• Rosyid, H. A., Widiyaningtyas, T., & Hadinata, N. F. (2019). Implementation of the exponential smoothing method for forecasting food prices at provincial levels on java Island. In 2019 Fourth International Conference on Informatics and Computing (ICIC) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/ICIC47613.2019.8985872

• Saba, T., Abunadi, I., Shahzad, M. N., & Khan, A. R. (2021). Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types. Microscopy Research and Technique, 84(7), 1462-1474. https://doi.org/10.1002/jemt.23702

• Sabu, K. M., & Kumar, T. M. (2020). Predictive analytics in agriculture: Forecasting prices of Arecanuts in Kerala. Procedia Computer Science, 171, 699-708. https://doi.org/10.1016/j.procs.2020.04.076

• Şahinli, M. A. (2020). Potato price forecasting with Holt-Winters and ARIMA methods: A case study. American Journal of Potato Research, 97(4), 336-346. https://doi.org/10.1007/s12230-020-09788-y

• Şahinli, M. A. (2021). Predicting and analyzing of Turkish sugar price with ARCH, GARCH, EGARCH and ARIMA methods. Scientific Papers: Management, Economic Engineering in Agriculture & Rural Development, 21(3), 703-712.

• Sanusi, O. I., Safi, S. K., Adeeko, O., & Tabash, M. I. (2022). Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria. Agricultural and Resource Economics: International Scientific E-Journal, 8(2), 124-140. https://doi.org/10.22004/ag.econ.322724

• Septiani, Y., & Setyowati, V. A. (2021). The ARIMA Box-Jenkins method has been used to predict the price of large curly red chilis. Gorontalo Development Review, 4(2), 89-101.

• Shao, Y. E., & Dai, J. T. (2018). Integrated feature selection of ARIMA with computational intelligence approaches for food crop price prediction. Complexity, 2018, Article 1910520. https://doi.org/10.1155/2018/1910520

• Shaub, D. (2020). Fast and accurate yearly time series forecasting with forecast combinations. International Journal of Forecasting, 36(1), 116-120. https://doi.org/10.1016/j.ijforecast.2019.03.032

• Shaub, D., & Ellis, P. (2020). forecastHybrid: Convenient functions for ensemble time series forecasts [R package version 5.0.19]. https://cran.r-project.org/web/packages/forecastHybrid/index.html

• Staffini, A. (2022). Stock price forecasting by a deep convolutional generative adversarial network. Frontiers in Artificial Intelligence, 5, Article 837596. https://doi.org/10.3389/frai.2022.837596

• Talkhi, N., Fatemi, N. A., Ataei, Z., & Nooghabi, M. J. (2021). Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods. Biomedical Signal Processing and Control, 66, Article 102494. https://doi.org/10.1016/j.bspc.2021.102494

• Talwar, A., & Goyal, C. K. (2019). A comparative study of various exponential smoothing models for forecasting coriander price in Indian commodity market. International Bulletin of Management and Economics, 10, 143-155.

• Taofik, O. D., & Tiamiyu-Ibrahim, S. (2021). Autoregressive integration moving average (ARIMA) model for prices of selected grains in the South West Nigeria. Agricultural Science Digest-A Research Journal, 41(1), 35-41. https://doi.org/10.18805/ag.D-239

• Tharmarajah, T., & Gjesdal, B. (2020). Salmon Price Forecasting: A Comparison of Univariate and Multivariate Forecasting Methods (Master's thesis). Norwegian School of Economics, Norway. https://openaccess.nhh.no/nhh-xmlui/bitstream/handle/11250/2737169/masterthesis.pdf?sequence=1

• Volkov, A., Morkūnas, M., & Skvarciany, V. (2019). Is it possible to predict food retail prices? Evidence from Lithuanian market. In Proceedings of 6th International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering 2019 (pp. 181-187). VGTU Press. https://doi.org/10.3846/cibmee.2019.018

• Wahyuni, E. D., & Afandi, M. I. (2018). Price of rice forecasting based on pattern similarity. In Proceedings IJCST 2017 (Vol. 1, No. 1, pp. 543-548). Public Knowledge Project.

• Wang, J., Lu, S., Wang, S. H., & Zhang, Y. D. (2022). A review on extreme learning machine. Multimedia Tools and Applications, 81(29), 41611-41660. https://doi.org/10.1007/s11042-021-11007-7

• Wibowo, A., & Yasmina, I. (2021). Food price prediction using time series linear ridge regression with the best damping factor. Advances in Science, Technology and Engineering Systems Journal, 6(2), 694-698. https://doi.org/10.25046/aj060280

• Xu, X., & Zhang, Y. (2022a). Canola and soybean oil price forecasts via neural networks. Advances in Computational Intelligence, 2(5), 1-12. https://doi.org/10.1007/s43674-022-00045-9

• Xu, X., & Zhang, Y. (2022b). Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. Intelligent Systems in Accounting, Finance and Management, 29(3), 169-181. https://doi.org/10.1002/isaf.1519

• Yadav, V., & Nath, S. (2019). Novel hybrid model for daily prediction of PM10 using principal component analysis and artificial neural network. International Journal of Environmental Science and Technology, 16(6), 2839-2848. https://doi.org/10.1007/s13762-018-1999-x

• Yee, K. W., & Humaida, B. S. (2021). Comparison between artificial neural network and arima model in forecasting palm oil price in Malaysia. International Journal of Scientific Engineering and Science, 5, 12-15.

• Zhang, D., Zang, G., Li, J., Ma, K., & Liu, H. (2018). Prediction of soybean price in China using QR-RBF neural network model. Computers and Electronics in Agriculture, 154, 10-17. https://doi.org/10.1016/j.compag.2018.08.016

• Zhou, L. (2021). Application of ARIMA model on prediction of China’s corn market. Journal of Physics: Conference Series, 1941, Article 012064. https://doi.org/10.1088/1742-6596/1941/1/012064