The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality

Guo, Liang; Fang, Weiguo; Zhao, Qiuhong; Wang, Xu

doi:10.1016/j.cie.2021.107598

Cited by 52 publications

(14 citation statements)

References 40 publications

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“…Proposed by Drucker et al [ 51 ], support vector regression (SVR) is an extension of the support vector machine (SVM). SVR is a machine learning method that can be employed to study non-parametric estimation problems in limited-sample situations, making it suitable for small samples and non-linear problems [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Khorramifar

Sharabiani

Karami

et al. 2022

Foods

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Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

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Section: Methodsmentioning

confidence: 99%

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Khorramifar

Sharabiani

Karami

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…In another Chinese case study, Guo et al. [44] integrated Prophet to Support Vector Regression (SVR) to predict product demands. The proposed integrated model uses SVR to capture nonlinearities in the residuals generated by Prophet.…”

Section: The Proposed Integrated Approachmentioning

confidence: 99%

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

Souza

Nascimento

2022

Applied Soft Computing

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“…Nevertheless, shortterm forecastings are also important, especially in supporting operational decisions during the COVID-19 pandemic. Thus, classical parametric and machine learning models have also gained space during the pandemic, such as Autoregressive Integrated Moving Average (ARIMA) [21,[31][32][33][34][35][36][37], Holt-Winters [35][36][37][38][39][40], Prophet [20,36,[40][41][42], K-Nearest Neighbors (KNN) Regressor [37,[43][44][45], Random Forest Regressor (RFR) [11,16,46,47], and Support Vector Regressor (SVR) [16,37,40,[47][48][49]. Researchers may also choose two models [40,[43][44][45]47] or more than three models [16,36,37] to make the forecasts.…”

Section: Macapá Amapámentioning

confidence: 99%

“…Model performance and evaluation are key to assessing the model quality fit, measured by confronting actual values to the predicted ones [35]. To highlight, in the context of COVID-19, researchers have used many metrics to this end, such as the Root Mean Squared Error (RMSE) [33][34][35][36][37][38]46], Mean Absolute Percentage Error (MAPE) [34][35][36][37]39], Mean Absolute Error (MAE) [16,31,48], Mean Square Error (MSE) [40,46,48], Symmetric Mean Absolute Percentage Error (sMAPE) [16,43], Relative Root Mean Squared Error (RRMSE) [43,48], the Adjusted R-squared (R2) score [33,48], the Improvement Percentage (IP) [16,43], the Akaike Information Criterion (AIC) [32,38], and the Bayesian Information Criterion (BIC) [38].…”

Section: Macapá Amapámentioning

confidence: 99%

See 1 more Smart Citation

On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

et al. 2021

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Time series cross-validation is a technique to select forecasting models. Despite the sophistication of cross-validation over single test/training splits, traditional and independent metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are commonly used to assess the model’s accuracy. However, what if decision-makers have different models fitting expectations to each moment of a time series? What if the precision of the forecasted values is also important? This is the case of predicting COVID-19 in Amapá, a Brazilian state in the Amazon rainforest. Due to the lack of hospital capacities, a model that promptly and precisely responds to notable ups and downs in the number of cases may be more desired than average models that only have good performances in more frequent and calm circumstances. In line with this, this paper proposes a hybridization of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy sets to create a similarity metric, the closeness coefficient (CC), that enables relative comparisons of forecasting models under heterogeneous fitting expectations and also considers volatility in the predictions. We present a case study using three parametric and three machine learning models commonly used to forecast COVID-19 numbers. The results indicate that the introduced fuzzy similarity metric is a more informative performance assessment metric, especially when using time series cross-validation.

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The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality

Cited by 52 publications

References 40 publications

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

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