Arima Model for Forecasting the Price of Medium Quality Rice to Anticipate Price Fluctuations

Ohyver, Margaretha; Pudjihastuti, Herena

doi:10.1016/j.procs.2018.08.215

Cited by 42 publications

(20 citation statements)

References 3 publications

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“…Among data-driven approaches, statistical models which attempt to express the future values as a linear function of the historical data have been popular and widely used in many applications of time series prediction, such as wind energy generation [36]- [39], weather forecasting [40]- [42], market demand forecasting [43], [44] and nuclear component prognostics [10]. Erdem and Shi [36] used an autoregressive moving average (ARMA) for predicting wind speed and direction.…”

Section: Time Series Predictionmentioning

confidence: 99%

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Nguyen

Liu

Zio

2020

Applied Soft Computing

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Developing an accurate and reliable multi-step ahead prediction model is a key problem in many Prognostics and Health Management (PHM) applications. Inevitably, the further one attempts to predict into the future, the harder it is to achieve an accurate and stable prediction due to increasing uncertainty and error accumulation. In this paper, we address this problem by proposing a prediction model based on Long Short-Term Memory (LSTM), a deep neural network developed for dealing with the longterm dependencies in time-series data. Our proposed prediction model also tackles two additional issues. Firstly, the hyperparameters of the proposed model are automatically tuned by a Bayesian optimization algorithm, called Tree-structured Parzen Estimator (TPE). Secondly, the proposed model allows assessing the uncertainty on the prediction. To validate the performance of the proposed model, a case study considering steam generator data acquired from different French nuclear power plants (NPPs) is carried out. Alternative prediction models are also considered for comparison purposes.

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Section: Time Series Predictionmentioning

confidence: 99%

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Nguyen

Liu

Zio

2020

Applied Soft Computing

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“…Different models are used in data estimation. In agriculture domain, ARIMA model was used in many studies to forecast milled rice production (Suleman and Sarpong, 2012), to forecast rice area, production and productivity (Rahmanet al 2016;Hemavathi and Prabakaran, 2018), to forecast the price of medium quality rice to anticipate price fluctuations (Ohyver and Pudjihastuti, 2018), to forecast maize production (Sharma et al, 2018). In this study, ARIMA model is also used for data estimation.…”

Section: Methods Used In Data Analysismentioning

confidence: 99%

Forecasting of rice self-sufficiency in the Benin Republic using ARIMA model

Akouegnonhou

Demirbaş

2019

SJAFS

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Rice is one of the staple foods in the Benin Republic. Annual rice consumption is increasing faster than annual rice production. That is why Benin is not yet self-sufficient in rice production. To meet the local demand, huge quantities of rice are imported. For country planning, forecasting is the main tool for predicting rice variables. This paper describes an empirical study that used a time series modeling approach (Box-Jenkins' ARIMA model) to forecast rice production, rice consumption, rice importation, rice exportation and finally rice self-sufficiency in Benin. Based on ARIMA model, the rice self-sufficiency rate in Benin is forecasted to be 47%, 56%, 58%, 59% and 68% respectively in 2019, 2020, 2021, 2022 and 2023. The forecasts would be helpful for the policy makers to foresee ahead of time the future requirements of rice production, adopt appropriate measures to develop rice sector for effective rice self-sufficiency and to reduce rice importation.

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“…4  'p'order of auto-regression  'd'degree of first differential  'q'order of moving average (4) The moving average model of order 'q' is represented in Eqn. (5).…”

Section: Methodsologymentioning

confidence: 99%

“…Eventually, accurate forecasting allows industries/ government to maximize the profit. Presently, serious measures are being taken by governments to address issues of national emergency [5]. Presence of large amount of data has mandated the usage of computers in every industry [6].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Techniques for Sales of Spare Parts

A M*,

S P*,

David P

et al. 2019

IJRTE

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Forecasting plays a significant role in planning the future activities of an organization. An effective trade-off is achieved between inventory management and catering demands through an accurate forecast. A detailed study on procurement and planning processes has been conducted in this study. The need for a decision making statistical tool to forecast sales data of spare parts is the main area of focus. Spare part sales pattern remains to be undetermined as it does not follow a specific trend or seasonality. Statistical programming has been performed using ‘R Studio’ to analyse the monthly sales performance. The process is found to improve when a weekly order inflow is considered. ARIMA model is found to improve the accuracy of forecast by 40 percent. Also, accuracy of forecasting performed considering weekly order inflow was higher than that obtained by considering monthly inflow.

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Arima Model for Forecasting the Price of Medium Quality Rice to Anticipate Price Fluctuations

Cited by 42 publications

References 3 publications

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Forecasting of rice self-sufficiency in the Benin Republic using ARIMA model

Forecasting Techniques for Sales of Spare Parts

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