Ayush Jain, Smit Marvaniya, Shantanu Godbole, Vitobha Munigala
arXiv 9 Sep 2020 · Statistics — Applications · 4 citations (OpenAlex)
arXiv:2009.04171 · PDF · DOI · OpenAlex · Extracted main text
Accuracy of crop price forecasting techniques is important because it enables the supply chain planners and government bodies to take appropriate actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered every day, which can be prone to human-induced errors like the 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. We additionally propose a framework for context-based model selection and retraining considering factors such as model stability, data quality metrics, and trend analysis of crop prices. To show the efficacy of the proposed approach, we show experimental results on two crops - Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach not only improves accuracy metrics significantly when compared against the standard forecasting techniques but also provides robust models.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. J. Taylor and B. Letham, “Forecasting at scale,” The American Sta… (2018) Forecasting at scale | 0.874 | 5 | 2 | 100% |
| 2 | W. Ma, K. Nowocin, N. Marathe, and G. H. Chen, “An interpretable pro… (2018) An interpretable produce price forecasting system for small and marginal farmers in india using collaborative filtering and adap… | 0.644 | 2 | 2 | 100% |
| 3 | S. Siami-Namini and A. S. Namin, “Forecasting economics and financia… (2018) Forecasting economics and financial time series: Arima vs. lstm | 0.644 | 2 | 2 | 100% |
| 4 | S. I. Vagropoulos, G. Chouliaras, E. G. Kardakos, C. K. Simoglou, an… (2016) Comparison of sarimax, sarima, modified sarima and ann-based models for short-term pv generation forecasting | 0.585 | 3 | 1 | 100% |
| 5 | K. Yunus, T. Thiringer, and P. Chen, “Arima-based frequency-decompos… (2015) Arima-based frequency-decomposed modeling of wind speed time series | 0.585 | 3 | 1 | 100% |
| 6 | M. Shahhosseini, G. Hu, and S. V. Archontoulis, “Forecasting corn yi… (2020) Forecasting corn yield with machine learning ensembles | 0.511 | 2 | 1 | 100% |
| 7 | K. Amarasinghe, D. L. Marino, and M. Manic, “Deep neural networks fo… (2017) Deep neural networks for energy load forecasting | 0.405 | 1 | 1 | 100% |
| 8 | Y. Bao, Y. Lu, and J. Zhang, “Forecasting stock price by svms regres… (2004) Forecasting stock price by svms regression | 0.405 | 1 | 1 | 100% |
| 9 | V. Cerqueira, L. Torgo, F. Pinto, and C. Soares, “Arbitrated ensembl… (2017) Arbitrated ensemble for time series forecasting | 0.405 | 1 | 1 | 100% |
| 10 | G. Martin-Rodriguez and J. Caceres-Hernandez, “Canary tomato export… (2013) Canary tomato export prices: comparison and relationships between daily seasonal patterns | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.