[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods
EM de Oliveira, FLC Oliveira - Energy, 2018 - Elsevier
In the last decades, the world's energy consumption has increased rapidly due to
fundamental changes in the industry and economy. In such terms, accurate demand …
fundamental changes in the industry and economy. In such terms, accurate demand …
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
K Bandara, C Bergmeir, S Smyl - Expert systems with applications, 2020 - Elsevier
With the advent of Big Data, nowadays in many applications databases containing large
quantities of similar time series are available. Forecasting time series in these domains with …
quantities of similar time series are available. Forecasting time series in these domains with …
Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting
This paper discusses the application of ensemble techniques for the prediction of time
series, presenting an in‐depth review of the main techniques and algorithms used by the …
series, presenting an in‐depth review of the main techniques and algorithms used by the …
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
Exponential smoothing is one of the most popular forecasting methods. We present a
technique for the bootstrap aggregation (bagging) of exponential smoothing methods, which …
technique for the bootstrap aggregation (bagging) of exponential smoothing methods, which …
Exploring the sources of uncertainty: Why does bagging for time series forecasting work?
In a recent study, Bergmeir, Hyndman and Benítez (2016) successfully employed a
bootstrap aggregation (bagging) technique for improving the performance of exponential …
bootstrap aggregation (bagging) technique for improving the performance of exponential …
Machine learning vs statistical methods for time series forecasting: Size matters
Time series forecasting is one of the most active research topics. Machine learning methods
have been increasingly adopted to solve these predictive tasks. However, in a recent work …
have been increasingly adopted to solve these predictive tasks. However, in a recent work …
Price-taker bidding strategy under price uncertainty
AJ Conejo, FJ Nogales… - IEEE Transactions on …, 2002 - ieeexplore.ieee.org
This paper provides a framework to obtain the optimal bidding strategy of a price-taker
producer. An appropriate forecasting tool is used to estimate the probability density functions …
producer. An appropriate forecasting tool is used to estimate the probability density functions …
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 …
Predictability of monthly temperature and precipitation using automatic time series forecasting methods
We investigate the predictability of monthly temperature and precipitation by applying
automatic univariate time series forecasting methods to a sample of 985 40-year-long …
automatic univariate time series forecasting methods to a sample of 985 40-year-long …