Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation? [PDF]
Neural Comput Appl, 2022 Arcolezi HH +4 more
europepmc +1 more source
25 Years of IIF Time Series Forecasting: A Selective Review [PDF]
We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one
Jan G. De Gooijer, Rob J. Hyndman
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Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory. [PDF]
Energy (Oxf), 2021 Liu X, Lin Z.
europepmc +1 more source
Exploring ICA for time series decomposition [PDF]
In this paper, we apply independent component analysis (ICA) for prediction and signal extraction in multivariate time series data. We compare the performance of three different ICA procedures, JADE, SOBI, and FOTBI that estimate the components ...
Antonio García Ferrer +2 more
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The vector innovation structural time series framework: a simple approach to multivariate forecasting [PDF]
The vector innovation structural time series framework is proposed as a way of modelling a set of related time series. Like all multi-series approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts.
Ashton de Silva +2 more
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Forecasting time series with multivariate copulas
Dependence Modeling, 2015 Simard Clarence, Rémillard Bruno
doaj +1 more source
Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand [PDF]
In this paper we propose a new set of multivariate stochastic models that capture time varying seasonality within the vector innovations structural time series (VISTS) framework.
Ashton de Silva, George Athanasopoulos
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PRICE FORECASTING WITH TIME-SERIES METHODS AND NONSTATIONARY DATA: AN APPLICATION TO MONTHLY U.S. CATTLE PRICES [PDF]
The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of ...
Garcia, Philip, Zapata, Hector O.
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Partial Transfer Learning from Patch Transformer to Variate-Based Linear Forecasting Model
EnergiesTransformer-based time series forecasting models use patch tokens for temporal patterns and variate tokens to learn covariates’ dependencies. While patch tokens inherently facilitate self-supervised learning, variate tokens are more suitable for linear ...
Le Hoang Anh +7 more
doaj +1 more source
The State Space Models Toolbox for MATLAB [PDF]
State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy ...
John A. D. Aston, Jyh-Ying Peng
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