Seeking Accuracy in Forecasting Demand and Selling Prices: Comparison of Various Methods

The need for a good forecast estimate is imperative for managing flows in a supply chain. For this, it is necessary to make forecasts and integrate them into the flow control models, in particular in contexts where demand is very variable. However, forecasts are never reliable, hence the need to give a measure of the quality of these forecasts, by giving a measure of the forecast uncertainty linked to the estimate made. Different forecasting models have been developed in the past, particularly in the statistical area. Before going to our application on real industrial cases which highlights a prospective study of demand forecasting and a comparative study of sales price forecasts, we begin, in the first section of this chapter, by presenting the forecasting models, as well as their validation and monitoring.

Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

Flooding, a significant natural disaster, attracts worldwide attention because of its high impact on communities and individuals and increasing trend due to climate change. A flood forecast system can minimize the impacts by predicting the flood hazard before it occurs. Artificial neural networks (ANN) could efficiently process large amounts of data and find relations that enable faster flood predictions. The aim of this study is to perform multistep forecasts for 1–5 h after the flooding event has been triggered by a forecast threshold value. In this work, an ANN developed for the real-time forecast of flood inundation with a high spatial resolution (4 m × 4 m) is extended to allow for multiple forecasts. After trained with 120 synthetic flood events, the ANN was first tested with 60 synthetic events for verifying the forecast performance for 3 h, 6 h, 9 h and 12 h lead time. The model produces good results, as shown by more than 81% of all grids having an RMSE below 0.3 m. The ANN is then applied to the three historical flood events to test the multistep inundation forecast. For the historical flood events, the results show that the ANN outputs have a good forecast accuracy of the water depths for (at least) the 3 h forecast with over 70% accuracy (RMSE within 0.3 m), and a moderate accuracy for the subsequent forecasts with (at least) 60% accuracy.

Reproducing the long term predictions from Imperial College CovidSim Report 9

1AbstractWe present calculations using the CovidSim code which implements the Imperial College individual-based model of the COVID epidemic. Using the parameterization assumed in March 2020, we reproduce the predictions presented to inform UK government policy in March 2020. We find that CovidSim would have given a good forecast of the subsequent data if a higher initial value of R0 had been assumed. We then investigate further the whole trajectory of the epidemic, presenting results not previously published. We find that while prompt interventions are highly effective at reducing peak ICU demand, none of the proposed mitigation strategies reduces the predicted total number of deaths below 200,000. Surprisingly, some interventions such as school closures were predicted to increase the projected total number of deaths.

Application of SARIMA Model for Forecasting Consumption of Electricity in Gezira State, Sudan (2006-2018): تطبيقات نماذج ساريما للتنبؤ باستهلاك الكهرباء في ولاية الجزيرة, السودان (2006- 2018)

The purpose of the research is to reach the forecast of monthly electricity consumption in Gezira state, Sudan for the period (Jun 2018 - Dec 2020) through the application to the historical data of electric power consumption (Jan 2006-May 2018) obtained from the National Control Center, which has been applied in the research methodology of seasonal Autoregressive Integrated Moving Average due to seasonal behavior in the data, good forecast has been given by SARIMA (2, 1, 7) (0, 1, 1), which has been examined its quality using the Thiel coefficient. The study recommended the use of the model of seasonal Autoregressive Integrated Moving Average in data with Seasonal behavior due to its simple application and accuracy of the results reached.

Crystal Balls and Black Boxes: What Makes a Good Forecast?

As a discipline that concerns itself with the future, planning relies on forecasts to inform and guide action. With this reliance comes a concern that the best possible forecasts be produced. This review identifies three distinct ways in which forecasts may be evaluated (methodology, accuracy, and usefulness) and describes challenges associated with evaluating forecasts along any of these three dimensions. By way of example, this general discussion of forecasting is applied to the specific case of demand forecasts for transportation infrastructure, with an emphasis on transit infrastructure. There is a continuing need for planners to engage with interdisciplinary forecasting literature.

Optimasi Parameter Pemulusan Algoritma Brown Menggunakan Metode Golden Section Untuk Prediksi Data Tren Positif dan Negatif

Algoritma DES (Double Exponential Smoothing) Brown merupakan algoritma peramalan yang digunakan untuk memprediksi data deret berkala baik berpola tren positif maupun tren negatif. Namun algoritma ini mempunyai kelemahan yaitu dalam menentukan nilai parameter optimum untuk meminimasi error peramalan (MAPE), nilai parameter tersebut dicari menggunakan metode Golden Section dimana sebelumnya dicari secara manual menggunakan percobaan berulang kali. Penelitian ini menggunakan 60 data berpola tren yang dianalisis untuk pengelompokan pola data tren positif dan negatif dimana selanjutnya dilakukan proses peramalan, evaluasi dan pengujian untuk mengetahui jenis pola data tren apa yang terbaik. Dari hasil perhitungan dan pengujian diketahui bahwa parameter optimasi menghasilkan nilai MAPE yang optimum, dimana selanjutnya nilai parameter tersebut dilakukan proses peramalan pada kelompok pola data tren positif dan negatif yang menghasilkan rata-rata nilai MAPE sebesar 9,73401% (highly accurate) untuk data berpola tren positif dan 15,78467% (good forecast) untuk data berpola tren negatif. Algoritma peramalan DES Brown dengan metode optimasi parameter menghasilkan nilai pendekatan terhadap data asli jika data tersebut menunjukkan penambahan atau penurunan nilai disekitar nilai rata-rata. Sebaliknya, akan menghasilkan nilai MAPE yang tinggi (tidak akurat) jika data tersebut memiliki lonjakan periode nilai data. Dari kedua kelompok nilai MAPE tersebut dilakukan uji t statistik yang menyatakan bahwa data berpola tren positif () menghasilkan nilai rata-rata MAPE lebih baik dibandingkan data berpola tren negatif (μ2).

Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

Parameter C Optimizing for Robust ε-Support Vector Regression

In case of experimental data contaminated with errors and noise, the robust ε-support vector regression has good forecast accuracy and high generalization ability. However, it depends on the selection of system parameter. Firstly, this paper introduces the robust ε-support vector regression method. Secondly, as the experiments prove, the new method achieves high forecast accuracy by virtue of the optimal penalty parameter C. Finally, the optimal method of parameter C is presented in the last section.

Robustness Analysis of Novel ε-Support Vector Regression

The key to the robust ε-support vector regression algorithm is searching for the optimal regression hyper plane while data with disturbance in the X-direction. In the paper, the optimal regression hyper plane and the optimal separating hyper plane are compared and analyzed. By means of Kolmogorov test, it is can be deduced that the testing errors of the robust ε-support vector regression experiments follow normal distribution. The result demonstrates that the algorithm has good forecast accuracy and high robustness.