Results 51 to 60 of about 28,610,872 (373)
Testing for a Unit Root in Time Series Regression
, 1988 This Paper Proposes Some New Tests for Detecting the Presence of a Unit Root in Quite General Time Series Modesl. Our Approach Is Nonparametric with Respect to Nuisance Parameters and Thereby Allows for a Very Wide Class of Weakly Dependent and Possibly ...
P. Phillips
semanticscholar +1 more source
Multiscale reconstruction of time series [PDF]
, 2006 A new method is proposed which allows a reconstruction of time series based on higher order multiscale statistics given by a hierarchical process. This method is able to model the time series not only on a specific scale but for a range of scales.
A.P. Nawroth +18 more
core +2 more sources
Time-series forecasting with deep learning: a survey [PDF]
Philosophical Transactions of the Royal Society A, 2020 Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time ...
Bryan Lim, Stefan Zohren
semanticscholar +1 more source
Polynomial Regressions and Nonsense Inference
Econometrics, 2013 Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples.
Daniel Ventosa-Santaulària +1 more
doaj +1 more source
Bootstrap Methods for Time Series [PDF]
International Statistical Review, 2003 SummaryThe bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. The methods that are available for implementing the bootstrap and the accuracy of bootstrap estimates depend on whether the data are an independent random sample or a time series.
Härdle, Wolfgang +2 more
openaire +5 more sources
Clustering Macroeconomic Time Series
, 2017 The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved.
Augustyński, Iwo +1 more
core +1 more source
Forecasting with time series imaging
, 2020 Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting ...
Kang, Yanfei, Li, Feng, Li, Xixi
core +1 more source
From time series to superstatistics [PDF]
, 2005 Complex nonequilibrium systems are often effectively described by a `statistics of a statistics', in short, a `superstatistics'. We describe how to proceed from a given experimental time series to a superstatistical description.
Christian Beck +6 more
core +1 more source
Hybrid Time Series Method for Long-Time Temperature Series Analysis
Discrete Dynamics in Nature and Society, 2021 This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis.
Guangdong Huang, Jiahong Li
doaj +1 more source
Time series classification based on triadic time series motifs [PDF]
International Journal of Modern Physics B, 2019 It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series.
Wen-Jie Xie, Rui-Qi Han, Wei-Xing Zhou
openaire +3 more sources