DeepAI AI Chat
Log In Sign Up

Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics

09/11/2015
by   Jie Ding, et al.
Harvard University
0

Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes. We introduce a new model selection technique based on Gap statistics to learn the appropriate number of AR filters needed to model a time series. We define a new distance measure between stable AR filters and draw a reference curve that is used to measure how much adding a new AR filter improves the performance of the model, and then choose the number of AR filters that has the maximum gap with the reference curve. To that end, we propose a new method in order to generate uniform random stable AR filters in root domain. Numerical results are provided demonstrating the performance of the proposed approach.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/06/2015

Data-Driven Learning of the Number of States in Multi-State Autoregressive Models

In this work, we consider the class of multi-state autoregressive proces...
03/31/2020

On the the linear processes of a stationary time series AR(2)

Our aim in this work is to give explicit formula of the linear processes...
05/04/2016

Sampling Requirements for Stable Autoregressive Estimation

We consider the problem of estimating the parameters of a linear univari...
08/12/2019

Moments of Maximum: Segment of AR(1)

Let X_t denote a stationary first-order autoregressive process. Consider...
03/10/2018

ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting

Accurate demand forecasts can help on-line retail organizations better p...
05/31/2022

VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting

Time series models aim for accurate predictions of the future given the ...