Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …
energy systems and economics. Creating a forecasting model for a specific use case …
Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements
N Kashpruk, C Piskor-Ignatowicz, J Baranowski - Applied Sciences, 2023 - mdpi.com
Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0),
offering a crucial analytical tool for the vast data streams generated by modern industrial …
offering a crucial analytical tool for the vast data streams generated by modern industrial …
Machine learning-based time series models for effective CO2 emission prediction in India
China, India, and the USA are the countries with the highest energy consumption and CO 2
emissions globally. As per the report of datacommons. org, CO 2 emission in India is 1.80 …
emissions globally. As per the report of datacommons. org, CO 2 emission in India is 1.80 …
Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition
SR Moreno, LO Seman, SF Stefenon… - Energy, 2024 - Elsevier
Due to technological advancements, wind energy has emerged as a prominent renewable
power source. However, the intermittent nature of wind poses challenges in accurately …
power source. However, the intermittent nature of wind poses challenges in accurately …
Machine fault detection using a hybrid CNN-LSTM attention-based model
The predictive maintenance of electrical machines is a critical issue for companies, as it can
greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this …
greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this …
Unveiling the influence of artificial intelligence and machine learning on financial markets: A comprehensive analysis of AI applications in trading, risk management …
This study explores the adoption and impact of artificial intelligence (AI) and machine
learning (ML) in financial markets, utilizing a mixed-methods approach that includes a …
learning (ML) in financial markets, utilizing a mixed-methods approach that includes a …
[HTML][HTML] Machine learning sentiment analysis, COVID-19 news and stock market reactions
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the
influence of related news on the financial markets, especially during the early stage of the …
influence of related news on the financial markets, especially during the early stage of the …
Stock price forecasting by a deep convolutional generative adversarial network
A Staffini - Frontiers in artificial intelligence, 2022 - frontiersin.org
Stock market prices are known to be very volatile and noisy, and their accurate forecasting is
a challenging problem. Traditionally, both linear and non-linear methods (such as ARIMA …
a challenging problem. Traditionally, both linear and non-linear methods (such as ARIMA …
Time series big data: a survey on data stream frameworks, analysis and algorithms
Big data has a substantial role nowadays, and its importance has significantly increased
over the last decade. Big data's biggest advantages are providing knowledge, supporting …
over the last decade. Big data's biggest advantages are providing knowledge, supporting …
Forecasting hotel demand for revenue management using machine learning regression methods
LN Pereira, V Cerqueira - Current Issues in Tourism, 2022 - Taylor & Francis
This paper compares the accuracy of a set of 22 methods for short-term hotel demand
forecasting for lead times up to 14 days ahead. Machine learning models are compared with …
forecasting for lead times up to 14 days ahead. Machine learning models are compared with …