Book ChapterDOI
Fitting autoregressive models for prediction
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This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models in a stationary time series.Abstract:
This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models. The use of autoregressive representation of a stationary time series (or the innovations approach) in the analysis of time series has recently been attracting attentions of many research workers and it is expected that this time domain approach will give answers to many problems, such as the identification of noisy feedback systems, which could not be solved by the direct application of frequency domain approach [1], [2], [3], [9].read more
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Testing for a Unit Root in Time Series With Pretest Data-Based Model Selection
TL;DR: In this article, the authors examined the impact of data-based lag-length estimation on the behavior of the augmented Dickey-Fuller (ADF) test for a unit root and derived conditions under which the ADF test converges to the distribution tabulated by Dickey and Fuller.
Journal ArticleDOI
Semiparametric Estimates of the Relation between Weather and Electricity Sales
TL;DR: In this article, a nonlinear relationship between electricity sales and temperature is estimated using a semiparametric regression procedure that easily allows linear transformations of the data and accommodates introduction of covariates, timing adjustments due to the actual billing schedules, and serial correlation.
Journal ArticleDOI
Maximum entropy spectral analysis and autoregressive decomposition
Tad J. Ulrych,Thomas N. Bishop +1 more
TL;DR: The duality between the maximum entropy method (MEM) and the autoregressive representation of the data allows the application of recent advances in AR analysis to MEM in an attempt to obviate some shortcomings in this method of spectral decomposition as mentioned in this paper.
Proceedings ArticleDOI
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
TL;DR: The proposed Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes significantly improves the prediction accuracy compared to other baseline methods.
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