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Towards Context-based Model Selection for Improved Crop Price Forecasting

Published: 08 January 2022 Publication History

Abstract

Accuracy of crop price forecasting techniques plays an important role in enabling the supply chain planners and government bodies to take necessary actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered, which can be prone to human-induced errors like entry of incorrect data or entry of no data for many days. In addition to such human prone errors, the fluctuations in the prices itself make the creation of stable and robust forecasting solution a challenging task. Considering such complexities in crop price forecasting, in this paper, we present techniques to build robust crop price prediction models considering various features such as (i) historical price and market arrival quantity of crops, (ii) historical weather data that influence crop production and transportation, (iii) data quality-related features obtained by performing statistical analysis. Furthermore, we propose a crop-specific context-based model selection strategy using trend analysis to deal with high fluctuations in crop prices. To show the efficacy of the proposed approach, we show experimental results using two different time-series feature representations on two crops - Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach provides improved accuracy metrics when compared to standard forecasting techniques.

References

[1]
Ayodele Ariyo Adebiyi, Aderemi Oluyinka Adewumi, and Charles Korede Ayo. 2014. Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics 2014 (2014).
[2]
Agmarknet. [n.d.]. Agricultural Marketing Information System. https://agmarknet.gov.in/.
[3]
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, 2019. Gluonts: Probabilistic time series models in python. arXiv preprint arXiv:1906.05264(2019).
[4]
Kasun Amarasinghe, Daniel L Marino, and Milos Manic. 2017. Deep neural networks for energy load forecasting. (2017).
[5]
Yukun Bao, Yansheng Lu, and Jinlong Zhang. 2004. Forecasting stock price by SVMs regression. In ICOAI.
[6]
Giulio Barbato, EM Barini, Gianfranco Genta, and Raffaello Levi. 2011. Features and performance of some outlier detection methods. Journal of Applied Statistics(2011).
[7]
Yoshua Bengio and Nicolas Chapados. 2002. Metric-based model selection for time-series forecasting. In Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. IEEE, 13–22.
[8]
Vítor Cerqueira, Luís Torgo, Fábio Pinto, and Carlos Soares. 2017. Arbitrated ensemble for time series forecasting. In ECML PKDD.
[9]
Alionue Dieng. 2008. Alternative forecasting techniques for vegetable prices in Senegal. Senegalese journal of agricultural research(2008).
[10]
Andrea Fumi, Arianna Pepe, Laura Scarabotti, and Massimiliano M Schiraldi. 2013. Fourier analysis for demand forecasting in a fashion company. IJEBM (2013).
[11]
N Hemageetha and GM Nasira. 2013. Radial basis function model for vegetable price prediction. In PRIME.
[12]
Hansika Hewamalage, Christoph Bergmeir, and Kasun Bandara. 2021. Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting 37, 1 (2021), 388–427.
[13]
Heikki Junninen, Harri Niska, Kari Tuppurainen, Juhani Ruuskanen, and Mikko Kolehmainen. 2004. Methods for imputation of missing values in air quality data sets. Atmospheric Environment(2004).
[14]
Kyoung-jae Kim. 2003. Financial time series forecasting using support vector machines. Neurocomputing (2003).
[15]
Jiahan Li and Weiye Chen. 2014. Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models. International Journal of Forecasting(2014).
[16]
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR.
[17]
Bryan Lim, Sercan O Arik, Nicolas Loeff, and Tomas Pfister. 2019. Temporal fusion transformers for interpretable multi-horizon time series forecasting. arXiv preprint arXiv:1912.09363(2019).
[18]
Wei Ma, Kendall Nowocin, Niraj Marathe, and George H. Chen. 2018. An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbors. In ICTD ’19.
[19]
G Martin-Rodriguez and JJ Caceres-Hernandez. 2013. Canary tomato export prices: comparison and relationships between daily seasonal patterns. SJAR (2013).
[20]
Igor Melnyk and Arindam Banerjee. 2016. Estimating Structured Vector Autoregressive Models. In ICML.
[21]
G. M. Nasira and N. Hemageetha. 2012. Vegetable price prediction using data mining classification technique. PRIME (2012).
[22]
Ministry of Agriculture and Farmers. 2019. Annual Report 2018-19.
[23]
Mariana Oliveira and Luís Torgo. 2014. Ensembles for Time Series Forecasting. In ACML.
[24]
Hongbing Ouyang, Xiaolu Wei, and Qiufeng Wu. 2019. Agricultural commodity futures prices prediction via long-and short-term time series network. Journal of Applied Economics(2019).
[25]
Huitong Qiu, Sheng Xu, Fang Han, Han Liu, and Brian Caffo. 2015. Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes. In ICML’15.
[26]
Stephen Roberts, Michael Osborne, Mark Ebden, Steven Reece, Neale Gibson, and Suzanne Aigrain. 2013. Gaussian processes for time-series modelling. PHILOS T R SOC A (2013).
[27]
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2020. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting 36, 3 (2020), 1181–1191.
[28]
Giuseppe Schlitzer. 1995. Testing the stationarity of economic time series: further Monte Carlo evidence. Ricerche Economiche (1995).
[29]
Mohsen Shahhosseini, Guiping Hu, and Sotirios V Archontoulis. 2020. Forecasting corn yield with machine learning ensembles. arXiv:2001.09055 (2020).
[30]
Wen Shen, Vahan Babushkin, Zeyar Aung, and Wei Lee Woon. 2013. An ensemble model for day-ahead electricity demand time series forecasting. In Proceedings of the Fourth International Conference on Future energy systems.
[31]
Sima Siami-Namini and Akbar Siami Namin. 2018. Forecasting economics and financial time series: ARIMA vs. LSTM. arXiv preprint arXiv:1803.06386(2018).
[32]
Sean J Taylor and Benjamin Letham. 2018. Forecasting at scale. The American Statistician(2018).
[33]
Kevin Thomas. 2019. Time Series Prediction for Stock Price and Opioid Incident Location. Ph.D. Dissertation. Arizona State University.
[34]
Tao Xiong, Chongguang Li, and Yukun Bao. 2018. Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China. Neurocomputing (2018).
[35]
Murat Yercan and Hakan Adanacioglu. 2012. An analysis of tomato prices at wholesale level in Turkey: An application of SARIMA model. Custos e Agronegocio(2012).
[36]
Dabin Zhang, Shanying Chen, Ling Liwen, and Qiang Xia. 2020. Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons. IEEE Access (2020).
[37]
G Peter Zhang. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(2003), 159–175.

Cited By

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  • (2023)Forecasting Crop Price using various approaches of Machine Learning2023 International Conference on Innovations in Engineering and Technology (ICIET)10.1109/ICIET57285.2023.10220616(1-5)Online publication date: 13-Jul-2023
  • (2023)An Innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)10.1109/CASE56687.2023.10260494(1-7)Online publication date: 26-Aug-2023

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        cover image ACM Conferences
        CODS-COMAD '22: Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)
        January 2022
        357 pages
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        Published: 08 January 2022

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        1. Context-based Model Selection
        2. Crop Price Forecasting

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        • (2023)Forecasting Crop Price using various approaches of Machine Learning2023 International Conference on Innovations in Engineering and Technology (ICIET)10.1109/ICIET57285.2023.10220616(1-5)Online publication date: 13-Jul-2023
        • (2023)An Innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)10.1109/CASE56687.2023.10260494(1-7)Online publication date: 26-Aug-2023

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