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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Learning Discrepancy Models From Experimental Data.
TL;DR: This work uses the sparse identification of nonlinear dynamics (SINDy) algorithm to discover a model for the discrepancy between a simplified model and measurement data, assuming that the model mismatch can be sparsely represented in a library of candidate model terms.
Journal ArticleDOI
A fundamental relation between predictor identification and power spectrum estimation
TL;DR: In this paper, an asymptotic linear relation between the FPE (final prediction error) of the predictor obtained by using the least squares estimate of autoreggression coefficients and the integrated relative mean square error of the estimate of power spectrum obtained from the fitted autoregression model is established.
Journal ArticleDOI
A comparative study of wavelet-based ANN and classical techniques for geophysical time-series forecasting
TL;DR: Wavelet-based versions of these techniques, respectively denoted as W-MMSE, W-Kalman and W-ANN, rather than the original techniques themselves, have been found to possess better capabilities in forecasting the highly nonlinear geophysical data.
Proceedings ArticleDOI
A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis
TL;DR: A new and practical approach to determine the appropriate orders in an auto-regressive exogenous input (ARK) model is developed, and this technique is applied to a real marketing data, and dynamic response character of sales amount to advertisement and sales promotion is analyzed.
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Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi,Rishab Goel,Kshitij Jain,Ivan Kobyzev,Akshay Sethi,Peter Forsyth,Pascal Poupart +6 more
TL;DR: A survey of representation learning for dynamic knowledge graphs can be found in this article, where the authors describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category.
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