Application of SARIMA Model in Cucumber Price Forecast

Luo, Chang; Zhou, Li; Wei, Qinping

doi:10.4028/www.scientific.net/amm.373-375.1686

Cited by 24 publications

(6 citation statements)

References 1 publication

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“…In [9], a seasonal autoregressive integrated moving average model was used to forecast cucumber prices when accounting for seasonal variations. The experimental results show that the SARIMA model accurately predicts cucumber market prices in previous months.…”

Section: Related Workmentioning

confidence: 99%

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

2022

KSII TIIS

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Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for smallscale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

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Section: Related Workmentioning

confidence: 99%

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

2022

KSII TIIS

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“…The forecasting performance of different time series models such as SARIMA [58], Exponential Smoothing State Space (ETS) model [59] and Bagged Model [60] was evaluated for univariate data coming from the surface moisture sensor suite. The statistical performance metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square (RMSE) were used for evaluating the forecasting performance, which are defined in ( 5), ( 6) and ( 7) respectively.…”

Section: F Comparative Analysis Of Forecasting Modelsmentioning

confidence: 99%

Robust Sensor Suite Combined With Predictive Analytics Enabled Anomaly Detection Model for Smart Monitoring of Concrete Sewer Pipe Surface Moisture Conditions

et al. 2020

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Globally, the water industry considers microbial induced corrosion of concrete sewer pipes as a serious problem.There are reported analytical models and data analytic models that are used to predict the rate of corrosion throughout the sewer network. Those models incorporate surface moisture conditions of concrete sewer pipes as observations. Due to the unavailability of sensors to monitor concrete sewer surface moisture conditions, water utilities use surrogate measures such as relative humidity of the air as an observation for the model. Hence, the corrosion predictions are often hampered and associated with prediction uncertainties. In this context, this paper presents the development and successful evaluation of an electrical resistivity based sensor suite for estimating the surface moisture conditions of concrete sewer pipes. The sensor was deployed inside a municipal sewer pipe of Sydney city, Australia to carry out field measurements. The post-deployment study revealed the survival of the sensing system under hostile sewer conditions and demonstrated their suitability for long-term monitoring inside sewer pipes. Besides sensor development, a predictive analytics model was proposed for anomaly detection. The model incorporates a forecasting approach using a seasonal autoregressive integrated moving average technique for anomaly detection. The model was evaluated using the sensor data and results demonstrated its effective performance. Overall, the proposed sensor suite can ameliorate the way water utilities monitor sewer pipe corrosion.

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“…Forecasting vegetable prices is also a challenging task due to seasonal variation. Hence, Luo et al (2013) used the SARIMA model which considers the seasonal effect, to investigate an effective model of forecasting Cucumber price. SARIMA(1,0,1)(1,1,1) 12 model was selected as the best-fitted model which provides feasible short-term warning of vegetable price.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the red lentils commodity market price using SARIMA models

2020

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Canada is the world's largest producer of lentils, accounting for 32.8% of total production in the world. However, the production of lentils are prone to fluctuate due to the impact of erratic factors such as weather conditions and economic crises. Consequently, the price of the commodity will be changed and volatile. Therefore, the approach of modeling and forecasting future price based on the preceding data will provide representative figures to make decisions regarding the lentil production for growers and end users. Hence, the objective of this study is to model and forecast the red lentil prices using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). Eight years of weekly data starting from 2010 to 2019 which comprise 521 observations, obtained from Saskatchewan.ca were used in this study. The average red lentil price in Saskatchewan was dollar 24.75 per 100 lb, and weekly prices were highly fluctuating over time. The seasonality and volatility of red lentils are modeled and forecasted by calculating the seasonal index and applying SARIMA models to the time series. The results reveal that the SARIMA (2,1,2) (0,1,1)[52] model provides the best in sample and out-sample performance when predicting the red lentil prices. Hence, this model can be utilized by both growers and end users in making optimal production decisions and in managing overall price risk.

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Application of SARIMA Model in Cucumber Price Forecast

Cited by 24 publications

References 1 publication

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

Price Forecasting on a Large Scale Data Set using Time Series and Neural Network Models

Robust Sensor Suite Combined With Predictive Analytics Enabled Anomaly Detection Model for Smart Monitoring of Concrete Sewer Pipe Surface Moisture Conditions

Forecasting the red lentils commodity market price using SARIMA models

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