A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several
approaches that deal with this complex problem have been proposed in the literature but an …
approaches that deal with this complex problem have been proposed in the literature but an …
Critical review of data, models and performance metrics for wind and solar power forecast
Global climatic changes and increased carbon footprints provided the main impetus for the
decrease in the use of fossil fuels for electricity generation and transportation. Matured …
decrease in the use of fossil fuels for electricity generation and transportation. Matured …
An optimized model using LSTM network for demand forecasting
In a business environment with strict competition among firms, accurate demand forecasting
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Machine Learning (ML) methods have been proposed in the academic literature as
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …
Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model
The choice of the most promising algorithm to model and predict a particular phenomenon is
one of the most prominent activities of the temporal data forecasting. Forecasting (or …
one of the most prominent activities of the temporal data forecasting. Forecasting (or …
Monash time series forecasting archive
Many businesses and industries nowadays rely on large quantities of time series data
making time series forecasting an important research area. Global forecasting models that …
making time series forecasting an important research area. Global forecasting models that …
Machine learning strategies for time series forecasting
The increasing availability of large amounts of historical data and the need of performing
accurate forecasting of future behavior in several scientific and applied domains demands …
accurate forecasting of future behavior in several scientific and applied domains demands …
Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks
Day-ahead electricity prices are generally used as reference prices for decisions done in
energy trading, eg purchase and sale strategies are typically based on the day-ahead spot …
energy trading, eg purchase and sale strategies are typically based on the day-ahead spot …
[책][B] Practical time series forecasting with r: A hands-on guide
G Shmueli, J Polak - 2024 - books.google.com
Practical Time Series Forecasting with R: A Hands-On Guide, Third Edition provides an
applied approach to time-series forecasting. Forecasting is an essential component of …
applied approach to time-series forecasting. Forecasting is an essential component of …
A new fuzzy-based combined prediction interval for wind power forecasting
A Kavousi-Fard, A Khosravi… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty
associated with wind power generation in power systems. Since the forecasting errors …
associated with wind power generation in power systems. Since the forecasting errors …