Price Forecasting of Mango in Varanasi Market of Uttar Pradesh

Pardhi, Ravishankar; Singh, Rakesh; Paul, Ranjit Kumar

doi:10.12944/carj.6.2.12

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…Their results showed large variations between the forecast values and real values, and Holt Winter's model that considers seasonality showed the best performance. Apart from these studies, Darekar and Reddy [15], Jadhav et al [16], and Pardhi et al [17] forecast agricultural prices using the ARIMA model.…”

Section: Agricultural Price Forecasting Using Statistical Methodsmentioning

confidence: 99%

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Yin

Dong

et al. 2020

Agriculture

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It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

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Section: Agricultural Price Forecasting Using Statistical Methodsmentioning

confidence: 99%

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Yin

Dong

et al. 2020

Agriculture

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“…Al comparar los datos observados con la estimación del modelo de componentes no observados para los precios de maracuyá y granadilla, como lo muestra el Gráfico IX, se puede observar que el componente de tendencia del maracuyá es negativo (figura de la izquierda), mientras para la granadilla la tendencia de los precios estimados por el modelo es baja. Estos resultados difieren de los encontrados en el mercado de mango en Varanasi, que reporta un incremento en los precios que beneficia significativamente a los productores (Pardhi et al, 2018).…”

Section: Resultados De Componentes No Observados Y Regresión Markovianaunclassified

“…XXVII, No. especial 4 , 2021 ___________________________________________________________________197-212 Licencia de Creative Commons Atribución 4.0 Internacional (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.es debido a que la desviación del pronóstico obtenido con respecto a los valores observados no es muy alta, se constituyen en herramienta confiables para proyectar precios a futuro de frutas y verduras (Boateng et al, 2017;Jadhav et al, 2017;Pardhi, Singh y Kumar, 2018;Dharavath y Khosla, 2019;Paredes-García et al, 2019;Weng et al, 2019).…”

Section: Introductionunclassified

Modelos de fluctuaciones de precios agrícolas: Estudio comparativo de frutas tropicales frescas en Colombia

2021

RCS

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The cultivation of passion fruit (Passiflora Edulis Sims F. Purpurea and F. Flavicarpa) and granadilla (Passiflora Ligularis L.) are of great economic and productive interest in tropical countries, which is expressed by the sustained increase of the planting area to meet the growing demand. In order to minimize economic risks for investors, it is necessary to carry out adequate planning of production processes, estimating price fluctuations. The main objective of this article is to analyze price fluctuations, based on ARIMA (Autoregressive Integrated Moving Average) methods; unobserved components model and Markovian regression models, to establish the best performance through the minimization of the Akaike information criterion. To understand the stochastic phenomenon of the price time series, the data series from official statistics of these crops in an observation window between the years 2011-2019 were analyzed. The results showed that ARIMA models explain price fluctuations better and more accurately. It was concluded that this model is an ideal tool to analyze price behavior, contributing to price prediction, which is valuable information for decision makers.

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“…They are usually classified as traditional statistical methods. The ARIMA model is exploited by researchers [9][10][11][12] for agricultural price prediction. When studying cocoa bean price forecasting, Assis and Remali [13] tried to figure out the best method in various time-series prediction models.…”

Section: Price Forecasting Using Statistical Methodsmentioning

confidence: 99%

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Piao

Zhao

et al. 2021

Agriculture

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Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

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Price Forecasting of Mango in Varanasi Market of Uttar Pradesh

Cited by 9 publications

References 7 publications

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Modelos de fluctuaciones de precios agrícolas: Estudio comparativo de frutas tropicales frescas en Colombia

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

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