[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 …
Short-term wind power forecasting approach based on Seq2Seq model using NWP data
Y Zhang, Y Li, G Zhang - Energy, 2020 - Elsevier
Wind power is one of the main sources of renewable energy. Precise forecast of the power
output of wind farms could greatly decrease the negative impact of wind power on power …
output of wind farms could greatly decrease the negative impact of wind power on power …
The zwicky transient facility: science objectives
Abstract The Zwicky Transient Facility (ZTF), a public–private enterprise, is a new time-
domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope …
domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope …
Time-series clustering–a decade review
Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …
about classes. With emerging new concepts like cloud computing and big data and their vast …
catch22: CAnonical Time-series CHaracteristics: Selected through highly comparative time-series analysis
Capturing the dynamical properties of time series concisely as interpretable feature vectors
can enable efficient clustering and classification for time-series applications across science …
can enable efficient clustering and classification for time-series applications across science …
Evaluating time series forecasting models: An empirical study on performance estimation methods
Performance estimation aims at estimating the loss that a predictive model will incur on
unseen data. This process is a fundamental stage in any machine learning project. In this …
unseen data. This process is a fundamental stage in any machine learning project. In this …
k-shape: Efficient and accurate clustering of time series
The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …
substantial interest in the analysis and mining of time series. Clustering is one of the most …
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
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 …
The trilemma among CO2 emissions, energy use, and economic growth in Russia
This paper examines the relationship among CO2 emissions, energy use, and GDP in
Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses …
Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses …
Distributed and parallel time series feature extraction for industrial big data applications
The all-relevant problem of feature selection is the identification of all strongly and weakly
relevant attributes. This problem is especially hard to solve for time series classification and …
relevant attributes. This problem is especially hard to solve for time series classification and …