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Long term and short term forecasting of horticultural produce based on the LSTM network model

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Abstract

Forecasting the price of agricultural produce helps grower decide planting, harvesting, and trading time. Price forecasting of crops has garnered many researchers’ attention, hence plenty of forecasting models already exist in literature. Price forecasting of horticultural products is still a lesser explored area for researchers. Pricing strategy of vegetables do not follow the same strategies as that of crops. Due to seasonality and short lifetime, maintenance of vegetables differ from other agricultural products. As horticultural products are generated in a very short time after plantation, close forecasting of price may provide farmers more profit by choosing vegetables for plantation and guiding the appropriate harvesting time. In this paper, we have analyzed the performance of some machine learning and statistical models in this front. The error metrics like Root Mean Square Error(RMSE), Mean Absolute Error(MAE), and Mean Absolute Percentage Error (MAPE) have been used to study the performance of the models. We have proposed a Long Short-Term Memory(LSTM) based model for long-term price forecasting of vegetables like cabbage, Cauliflower, and Brinjal for some Indian markets. Friedman test and Wilcoxon signed rank test are used to analyze the employed models’ similarity and dissimilarity. The experiment results indicate that the proposed model outshines other models. A short-term price forecasting model has also been experimented with in this paper.

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References

  1. Abdul-Lateef Balogun Fatemeh Rezaie QBPLGSDYAAMPSTYSL (2021) Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (svr) with gwo, bat and coa algorithms. Geosci Front 12:15

    Article  Google Scholar 

  2. Anggraeni W, Andri KB, Mahananto F et al (2017) The performance of arimax model and vector autoregressive (var) model in forecasting strategic commodity price in indonesia. Procedia Computer Science 124:189–196

    Article  Google Scholar 

  3. Ayankoya K, Calitz AP, Greyling JH (2016) Real-time grain commodities price predictions in south africa: a big data and neural networks approach. Agrekon 55(4):483–508

    Article  Google Scholar 

  4. Bhattacharya G, Ghosh K, Chowdhury AS (2017) Granger causality driven ahp for feature weighted knn. Pattern Recogn 66:425–436

    Article  Google Scholar 

  5. Budi Cahyo Suryo PS, Wayan Mustika I, Wahyunggoro O, Wasisto HS (2019) Improved time series prediction using lstm neural network for smart agriculture application. In: 2019 5th International Conference on Science and Technology (ICST), vol. 1, pp. 1–4. https://doi.org/10.1109/ICST47872.2019.9166401

  6. Castellon DF, Fenerci A, ØIseth O (2021) A comparative study of wind-induced dynamic response models of long-span bridges using artificial neural networks, support vector regression and buffeting theory. J Wind Eng Ind Aerodyn 209:104484

    Article  Google Scholar 

  7. Chen S, Li B, Cao J, Mao B (2018) Research on agricultural environment prediction based on deep learning. Procedia computer science 139:33–40

    Article  Google Scholar 

  8. Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems. Man, and Cybernetics 4:325–327

    Article  Google Scholar 

  9. Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: a survey. Comput Electron Agric 155:257–282

    Article  Google Scholar 

  10. Guidolin M (2016) Modelling, estimating and forecasting financial data under regime (markov) switching. Retrivied from http://didattica.unibocconi.it/mypage/dwload.php

  11. Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica:, Journal of the econometric society, pp 357–384

  12. Hansen BE (1992) The likelihood ratio test under nonstandard conditions: testing the markov switching model of gnp. Journal of applied Econometrics 7(S1):S61–S82

    Article  Google Scholar 

  13. Haque A (2012) The effects of exchange rate and commodity price volatilities on trade volumes of major agricultural commodities. Ph.D thesis

  14. Hernandez-Matamoros A, Fujita H, Hayashi T, Perez-Meana H (2020) Forecasting of covid19 per regions using arima models and polynomial functions. Applied soft computing 106610:96

    Google Scholar 

  15. Herrera R, Rodriguez A, Pino G (2017) Modeling and forecasting extreme commodity prices: a markov-switching based extreme value model. Energy Economics 63:129–143

    Article  Google Scholar 

  16. Huan J, Li H, Li M, Chen B (2020) Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: a study of chang zhou fishery demonstration base, china. Comput Electron Agric 105530: 175

    Google Scholar 

  17. Ighravwe DE, Mashao D (2020) Analysis of support vector regression kernels for energy storage efficiency prediction. Energy Reports 6:634–639

    Article  Google Scholar 

  18. Jung DH, Kim HS, Jhin C, Kim HJ, Park SH (2020) Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Comput Electron Agric 173:105402

    Article  Google Scholar 

  19. Karasu S, Altan A (2019) Recognition model for solar radiation time series based on random forest with feature selection approach. In: 2019 11Th international conference on electrical and electronics engineering (ELECO), pp. 8–11. IEEE

  20. Khandelwal I, Adhikari R, Verma G (2015) Time series forecasting using hybrid arima and ann models based on dwt decomposition. Procedia Computer Science 48:173–179

    Article  Google Scholar 

  21. Kuan CM (2002) Lecture on the markov switching model. Institute of Economics Academia Sinica 8(15):1–30

    Google Scholar 

  22. Le T, Vo B, Fujita H, Nguyen NT, Baik SW (2019) A fast and accurate approach for bankruptcy forecasting using squared logistics loss with gpu-based extreme gradient boosting. Inf Sci 494:294–310

  23. Lewis RJ (2000) An introduction to classification and regression tree (cart) analysis. In: Annual meeting of the society for academic emergency medicine in san francisco, california, vol. 14

  24. Li B, Ding J, Yin Z, Li K, Zhao X, Zhang L (2021) Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting. Expert Systems with Applications 168:114232. https://doi.org/10.1016/j.eswa.2020.114232https://doi.org/10.1016/j.eswa. https://doi.org/10.1016/j.eswa.2020.1142322020.114232. https://www.sciencedirect.com/science/article/pii/S0957417420309520https://www.sciencedirect.com/science/article/pii/ https://www.sciencedirect.com/science/article/pii/S0957417420309520S0957417420309520

    Article  Google Scholar 

  25. Liu Y, Yang C, Huang K, Gui W (2020) Non-ferrous metals price forecasting based on variational mode decomposition and lstm network. Knowl-Based Syst 188:105006

    Article  Google Scholar 

  26. Loh WY (2014) Fifty years of classification and regression trees. Int Stat Rev 82(3):329–348

    Article  MathSciNet  Google Scholar 

  27. Malik M, Bansal M, Agarwal R, Kanojia A, Singh R (2017) Crop selection algorithm-technique for price prediction. Int. J. Res. Econ. Soc. Sci 7:205–211

    Google Scholar 

  28. Manogarathash, MP., Gamage, A., Kasthurirathna, D.: Agro-genius: Crop prediction using machine learning

  29. Maruotti A, Petrella L, Sposito L (2021) Hidden semi-markov-switching quantile regression for time series. Computational Statistics & Data Analysis 159:107208

    Article  MathSciNet  Google Scholar 

  30. Meza-Pale P, Yunez-Naude A (2015) The effect of rainfall variation on agricultural households: evidence from mexico. Tech rep

  31. Moore P, Lyons T, Gallacher J, Initiative ADN (2019) Random forest prediction of alzheimer’s disease using pairwise selection from time series data. Plos one 14(2) e0211558

  32. Noonari S, Irfana N, Raiz A, Muhammad I, Ali S (2015) Price flexibility and seasonal variations of major vegetables in sindh pakistan. J Food Process Technol 6(524):2

    Google Scholar 

  33. Oda T, Barolli A, Spaho E, Barolli L, Xhafa F (2014) Analysis of mesh router placement in wireless mesh networks using friedman test. In: 2014 IEEE 28Th international conference on advanced information networking and applications, pp. 289–296. IEEE

  34. Ouyang H, Wei X, Wu Q (2019) Agricultural commodity futures prices prediction via long-and short-term time series network. J Appl Econ 22(1):468–483

    Article  Google Scholar 

  35. Putri Y, Adam N, Aziz A (2019) Rice yield prediction model with respect to crop healthiness and soil fertility. Food Research 2:174–180

    Google Scholar 

  36. Qiu X, Zhang L, Suganthan PN, Amaratunga GA (2017) Oblique random forest ensemble via least square estimation for time series forecasting. Inf Sci 420:249–262

    Article  Google Scholar 

  37. Rachana R, Shravani S, Kousar S (2019) Crop price forecasting system using supervised machine learning algorithms. International Research Journal of Engineering and Technology (IRJET), p 6

  38. Rajeswari, S., Suthendran, K.: Developing an agricultural product price prediction model using hadt algorithm

  39. Ribeiro MHDM, dos Santos Coelho L (2020) Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Appl Soft Comput 86:105837

    Article  Google Scholar 

  40. Rohith V, Kishore DC (2020) Crop price prediction and forecasting system using supervised machine learning algorithms. International Journal of Advanced Research in Computer and Communication Engineering, p 9

  41. Sakamoto S, Lala A, Oda T, Kolici V, Barolli L, Xhafa F (2015) Analysis of wmn-hc simulation system data using friedman test. In: 2015 Ninth international conference on complex, intelligent, and software intensive systems, pp. 254–259. IEEE

  42. Setiawan IN, Kurniawan R, Yuniarto B, Caraka RE, Pardamean B (2021) Parameter optimization of support vector regression using harris hawks optimization. Procedia Computer Science 179:17–24

    Article  Google Scholar 

  43. Shahwan T, Odening M (2007) Forecasting agricultural commodity prices using hybrid neural networks. In: Computational intelligence in economics and finance, pp. 63–74. Springer

  44. Ticlavilca A, Feuz DM, Mckee M (2010) Forecasting agricultural commodity prices using multivariate bayesian machine learning regression

  45. Torres M, Howitt R, Rodrigues L (2019) Analyzing rainfall effects on agricultural income: Why timing matters. EconomiA 20(1):1–14

    Article  Google Scholar 

  46. Varun N, Sahana S (2019) Muddasir: Agriculture commodity price forecastingusing ml techniques. International Journal of Innovative Technology and Exploring Engineering (IJITEE), p 9

  47. Wang J, Wang Z, Li X, Zhou H (2019) Artificial bee colony-based combination approach to forecasting agricultural commodity prices International Journal of Forecasting

  48. Wang L, Khan L, Thuraisingham B (2008) An effective evidence theory based k-nearest neighbor (knn) classification. In: 2008 IEEE/WIC/ACM International conference on web intelligence and intelligent agent technology, vol. 1, pp. 797–801. IEEE

  49. Weng Y, Wang X, Hua J, Wang H, Kang M, Wang FY (2019) Forecasting horticultural products price using arima model and neural network based on a large-scale data set collected by web crawler. IEEE Transactions on Computational Social Systems 6(3):547–553

    Article  Google Scholar 

  50. Wu H, Cai Y, Wu Y, Zhong R, Li Q, Zheng J, Lin D, Li Y (2017) Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression Bioscience trends

  51. Wu H, Wu H, Zhu M, Chen W, Chen W (2017) A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in beijing. Journal of Big Data 4(1):1–22

    Article  Google Scholar 

  52. Xiong T, Li C, Bao Y, Hu Z, Zhang L (2015) A combination method for interval forecasting of agricultural commodity futures prices. Knowl-Based Syst 77:92–102

    Article  Google Scholar 

  53. Xu X, Zhang Y (2021) Corn cash price forecasting with neural networks. Computers and Electronics in Agriculture 184:106120. https://doi.org/10.1016/j.compag.2021.106120. https://www.sciencedirect.com/science/article/pii/S0168169921001381https://www.scie- https://www.sciencedirect.com/science/article/pii/S0168169921001381ncedirect.com/science/article/pii/S0168169921001381

    Article  Google Scholar 

  54. Xu Y, Shen S, Chen Z (2009) Research on forecast of sugar price based on improved neural network. In: 2009 Second international symposium on intelligent information technology and security informatics, pp. 12–15. IEEE

  55. Ye F, Zhang L, Zhang D, Fujita H, Gong Z (2016) A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf Sci 367:41–57

    Article  Google Scholar 

  56. Yue Y, Li JH, Fan LF, Zhang LL, Zhao PF, Zhou Q, Wang N, Wang ZY, Huang L, Dong XH (2020) Prediction of maize growth stages based on deep learning. Comput Electron Agric 172:105351

    Article  Google Scholar 

  57. Zhang D, Chen S, Liwen L, Xia Q (2020) Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons. IEEE Access 8:28197–28209. https://doi.org/10.1109/ACCESS.2020.2971591

    Article  Google Scholar 

  58. Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (lstm) based model for predicting water table depth in agricultural areas. Journal of hydrology 561:918–929

    Article  Google Scholar 

  59. Zhang Y, Na S (2018) A novel agricultural commodity price forecasting model based on fuzzy information granulation and mea-svm model. Math Probl Eng, p 2018

  60. Zimmerman DW, Zumbo BD (1993) Relative power of the wilcoxon test, the friedman test, and repeated-measures anova on ranks. J Exp Educ 62(1):75–86

    Article  Google Scholar 

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  1. National Institute of Technology, Durgapur, West Bengal, India

    Tumpa Banerjee, Shreyashee Sinha & Prasenjit Choudhury

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Banerjee, T., Sinha, S. & Choudhury, P. Long term and short term forecasting of horticultural produce based on the LSTM network model. Appl Intell 52, 9117–9147 (2022). https://doi.org/10.1007/s10489-021-02845-x

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