A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks
In the broad scientific field of time series forecasting, the ARIMA models and their variants
have been widely applied for half a century now due to their mathematical simplicity and …
have been widely applied for half a century now due to their mathematical simplicity and …
Forecasting Thailand's Transportation CO2 Emissions: A Comparison among Artificial Intelligent Models
Transportation significantly influences greenhouse gas emissions—particularly carbon
dioxide (CO2)—thereby affecting climate, health, and various socioeconomic aspects …
dioxide (CO2)—thereby affecting climate, health, and various socioeconomic aspects …
[PDF][PDF] Stock price forecast of macro-economic factor using recurrent neural network
The stock market is one of the investment choices that always have traction from time to time.
Aside from being a means of corporate funding, investing in the stock market can benefit …
Aside from being a means of corporate funding, investing in the stock market can benefit …
Improving the forecasting approach for crude oil sourcing at an oil refining firm: a decision theory perspective
A Gezdur, J Bhattacharjya… - Production Planning & …, 2024 - Taylor & Francis
Accurate price forecasting is pivotal for making informed sourcing decisions. This study
addresses this need by empowering logistics and supply planning managers at a leading …
addresses this need by empowering logistics and supply planning managers at a leading …
[Retracted] Design of Financial Management Model Using the Forward Neural Network Based on Particle Swarm Optimization Algorithm
Y Pan - Computational Intelligence and Neuroscience, 2022 - Wiley Online Library
The financial crisis of listed companies will bring huge losses to investors, so it is very
important to establish a financial early warning model for investors and other stakeholders …
important to establish a financial early warning model for investors and other stakeholders …
Stock market index prediction using transformer neural network models and frequency decomposition
In an increasingly complex and volatile environment, government officials, researchers, and
investors alike would like to possess models that accurately forecast markets in order to …
investors alike would like to possess models that accurately forecast markets in order to …
Finansal zaman serilerini tahminlemede kullanılan yöntemlere genel bir bakış
N Yurduseven, AA Müngen - Bilecik Şeyh Edebali Üniversitesi Fen …, 2022 - dergipark.org.tr
Geçmişte olduğu gibi günümüzde de yatırımcılar için finansal verilerin trendinin tahmin
edilebilmesi ve bu bilgi kullanılarak bir finansal strateji oluşturulması oldukça önemlidir …
edilebilmesi ve bu bilgi kullanılarak bir finansal strateji oluşturulması oldukça önemlidir …
[PDF][PDF] A hybrid deep learning model for air quality time series prediction
Air quality (mainly PM2. 5) forecasting plays an important role in the early detection and
control of air pollution. In recent times, numerous deep learning-based models have been …
control of air pollution. In recent times, numerous deep learning-based models have been …
[HTML][HTML] Estimating air methane and total hydrocarbon concentrations in Alberta, Canada using machine learning
R Luo, J Wang, I Gates - Atmospheric Pollution Research, 2024 - Elsevier
Fugitive emission sources are significant contributors to methane emissions, and time series
data on reported emissions from such sources remain underutilized. The Alberta Energy …
data on reported emissions from such sources remain underutilized. The Alberta Energy …
[PDF][PDF] Time series activity classification using gated recurrent units
The population of elderly is growing and is projected to outnumber the youth in the future.
Many researches on elderly assisted living technology were carried out. One of the focus …
Many researches on elderly assisted living technology were carried out. One of the focus …