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H. M. Emrul Kays, A. N. M. Karim, Mohd Radzi C. Daud, Maria L. R. Varela, Goran D. Putnik and José M. Machado
The adoption of forecasting approaches such as the multiplicative Holt-Winters (MHW) model is preferred in business, especially for the prediction of future events having seasonal and other causal variations. However, in the MHW model the initial values ...
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Simona Elena Dragomirescu,Daniela Cristina Solomon
One of the most important stages in the budget drafting process is the sales forecasting. As a matter of fact, the sales affect the whole activity of a company, their variation being considered the main risk factor for the performance and the financial p...
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Coskun Hamzaçebi
Forecasting electricity consumption is a very important issue for governments and electricity related foundations of public sector. Recently, Grey Modelling (GM (1,1)) has been used to forecast electricity demand successfully. GM (1,1) is useful when the...
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Natalí Carbo-Bustinza, Hasnain Iftikhar, Marisol Belmonte, Rita Jaqueline Cabello-Torres, Alex Rubén Huamán De La Cruz and Javier Linkolk López-Gonzales
In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these ...
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Yajun Wang, Jianping Zhu and Renke Kang
Seasonal?trend-decomposed transformer has empowered long-term time series forecasting via capturing global temporal dependencies (e.g., period-based dependencies) in disentangled temporal patterns. However, existing methods design various auto-correlatio...
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Abir HASSAN,Mahbubul Md. ALAM,Azmaine FAEIQUE
Pág. 25 - 43
The objective of this study is to forecast the trend of inflation in Bangladesh by utilizing past inflation data. To achieve this objective, we employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which is an extension of the Aut...
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Chul-Gyum Kim, Jeongwoo Lee, Jeong Eun Lee and Hyeonjun Kim
This study examines the long-term climate predictability in the Seomjin River basin using statistical methods, and explores the effects of incorporating the duration of climate indices as predictors. A multiple linear regression model is employed, utiliz...
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Vadim Kramar and Vasiliy Alchakov
The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant can be used to calculate the optimal electricity consumpt...
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Irina Kochetkova, Anna Kushchazli, Sofia Burtseva and Andrey Gorshenin
Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One ...
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Patrícia Ramos, José Manuel Oliveira, Nikolaos Kourentzes and Robert Fildes
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Fahad Radhi Alharbi and Denes Csala
Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average wit...
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Genevieve Rigler, Zoi Dokou, Fahad Khan Khadim, Berhanu G. Sinshaw, Daniel G. Eshete, Muludel Aseres, Wendale Amera, Wangchi Zhou, Xingyu Wang, Mamaru Moges, Muluken Azage, Baikun Li, Elizabeth Holzer, Seifu Tilahun, Amvrossios Bagtzoglou and Emmanouil Anagnostou
Engaging youth and women in data-scarce, least developed countries (LDCs) is gaining attention in the Sustainable Development Goal (SDG) arena, as is using citizen science as a multi-faceted mechanism for data collection, engendering personal empowerment...
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Lorenzo Sangelantoni, Antonio Ricchi, Rossella Ferretti and Gianluca Redaelli
The purpose of the present study is to assess the large-scale signal modulation produced by two dynamically downscaled Seasonal Forecasting Systems (SFSs) and investigate if additional predictive skill can be achieved, compared to the driving global-scal...
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Ayesha Ubaid, Farookh Hussain and Muhammad Saqib
Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and m...
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Chunfeng Duan, Pengling Wang, Wen Cao, Xujia Wang, Rong Wu and Zhi Cheng
In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over Chin...
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Omar Haji Kombo, Santhi Kumaran, Yahya H. Sheikh, Alastair Bovim and Kayalvizhi Jayavel
Reliable seasonal prediction of groundwater levels is not always possible when the quality and the amount of available on-site groundwater data are limited. In the present work, a hybrid K-Nearest Neighbor-Random Forest (KNN-RF) is used for the predictio...
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Keqing Li, Changyong Liang, Wenxing Lu, Chu Li, Shuping Zhao and Binyou Wang
The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are nece...
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Eduardo Caro and Jesús Juan
In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation ...
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Abdelmonaem Jornaz and V. A. Samaranayake
Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with differen...
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Hugo Carrão, Gustavo Naumann, Emanuel Dutra, Christophe Lavaysse and Paulo Barbosa
Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of ...
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