نتایج جستجو برای: artificial neural networks anns auto regressive integrated moving average arima
تعداد نتایج: 1522067 فیلتر نتایج به سال:
Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With increase installed WWTPs worldwide, modeling and forecast their energy have become a critical factor in WWTP design meet environmental economic requirements. The accurate swift forecasting soft-sensors are not only supportive daily electric financial budgeting by practitioners on micro-scale, but also b...
Februari 2020 merupakan waktu awal warga Indonesia didiagnosa positif Covid-19. Hingga kini, penyakit yang dikarenakan oleh Virus Corona ini belum mereda, bahkan dinyatakan sebagai Pandemi Global. Tujuan penelitian adalah mencari model prediksi Time Series untuk jumlah kasus Covid-19 di salah satu kota dengan infeksi terbsesar yaitu Jakarta. Penelitian menggunakan data dari Open Data Jakarta re...
For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificia...
One of the most important issues in designing coastal and offshore structures is the prediction of wave and current forces on slender cylinders. Such forces are often considered as dominate loadings. Many analytical and empirical methods such as Morison equation have been suggested for estimation of waves and current forces. Such methods, however, have shown inaccuracies in predicting hydrodyna...
Forecasting of sea level fluctuations is a suitable tool for comprehensive management of the sea and the protection of coastal areas. On the other hand, application of time series analysis for forecasting purposes has been evaluated to be very appropriate. Therefore, two time series consisting monthly measured sea level data were used in the present research. The data have been recorded at two ...
Background and Aim: Artificial intelligence is a branch of computer science that has the ability of analyzing complex medical data. Using artificial intelligence is common in diagnosing, treating and taking care of patients. Warfarin is one of the most commonly prescribed oral anticoagulants. Determining the exact dose of warfarin needed for patients is one of the major challenges in the health...
Machine learning (ML) models, including artificial neural networks (ANN), generalized regression (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, paper propo...
Load forecasting is one of the main concerns for power utility companies. It plays a significant role in planning decisions, scheduling, operations, pricing, customer satisfaction, and system security. This helps smart companies deliver services more efficiently analyze their operations way that can help optimize performance. In this paper, we propose study different techniques: multiple linear...
This paper presents the use of times series AutoRegressive Integrated Moving Average ARIMA(p,d,q) model with interventions, and neural network back-propagation model in analyzing the behavior of sales in a medium size enterprise located in Rio Grande do Sul Brazil for the period January 1984 – December 2000. The forecasts obtained using the neural network back-propagation model were found to be...
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