Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

Menculini, Lorenzo; Marini, Andrea; Proietti, Massimiliano; Garinei, Alberto; Bozza, Alessio; Moretti, Cecilia; Marconi, Marcello

doi:10.3390/forecast3030040

Cited by 34 publications

(19 citation statements)

References 35 publications

(39 reference statements)

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“…Although the combination of CNN and LSTM methods produced the best cross-validation results, it was discovered they were computationally intensive to fit. Similar to previous work 43 , we found that Prophet was the most time-efficient and had comparable cross-validation scores as compared to STL + ARIMA. In the withheld testing sample, Prophet also outperformed both.…”

Section: Discussionsupporting

confidence: 87%

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Pandit

Jalal

Toma

et al. 2022

Sci Rep

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Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders.

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Section: Discussionsupporting

confidence: 87%

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Pandit

Jalal

Toma

et al. 2022

Sci Rep

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“…This method is built on two basic characteristics of past values and errors. Furthermore, the method utilizes historical data to determine the performance of the model by using estimated errors (Menculini et al 2021). Physics-based methodologies are based on the physical models that define and represent the behavior of the variables providing more reliable predictions for the future trend of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive digital twin for offshore wind farms

et al. 2023

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As wind turbines continue to grow in size, they are increasingly being deployed offshore. This causes operation and maintenance of wind turbines becoming more challenging. Digitalization is a key enabling technology to manage wind farms in hostile environments and potentially increasing safety and reducing operational and maintenance costs. Digital infrastructure based on Industry 4.0 concept, such as digital twin, enables data collection, visualization, and analysis of wind power analytic at either individual turbine or wind farm level. In this paper, the concept of predictive digital twin for wind farm applications is introduced and demonstrated. To this end, a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication is developed. The platform is completed with the Prophet prediction algorithm to detect potential failure of wind turbine components in the near future and presented in augmented reality to enhance user experience. The presentation is intuitive and easy to use. The limitations of the platform include a lack of support for specific features like electronic signature, enhanced failover, and historical data sources. Simulation results based on the Hywind Tampen floating wind farm configuration show our proposed platform has promising potentials for offshore wind farm applications.

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“…From the measurable examination side of the time series, the stationarity of the series of contrasts for an incorporated interaction is a basic quality. e model of nonstationary series is incorporated cycles [41].…”

Section: Arima Model For Minimizing the Error Gap In Smart Farmingmentioning

confidence: 99%

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Wawale

Jawarneh²,

et al. 2022

Journal of Food Quality

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Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives.

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Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

Cited by 34 publications

References 35 publications

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Predictive digital twin for offshore wind farms

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

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