Fitting State-space Model for Long-term Prediction of the Log-likelihood of Nonstationary Time Series Models

10/12/2022
by   Genshiro Kitagawa, et al.
0

The goodness of the long-term prediction in the state-space model was evaluated using the squared long-term prediction error. In order to estimate the model parameters suitable for long-term prediction, we devised a modified log-likelihood corresponding to the long-term prediction error variance. Trend models and seasonally adjusted models with and without AR component are examined as examples.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/21/2022

The Information Criterion GIC of Trend and Seasonal Adjustment Models

This paper presents an algorithm for computing the GIC and the TIC of th...
research
02/24/2023

TrafFormer: A Transformer Model for Prediction Long-term Traffic

Traffic prediction is a flourishing research field due to its importance...
research
11/14/2022

Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models

The field of deep-learning-based ECG analysis has been largely dominated...
research
02/22/2018

Deep learning algorithm for data-driven simulation of noisy dynamical system

We present a deep learning model, DE-LSTM, for the simulation of a stoch...
research
03/11/2021

Extracting candidate factors affecting long-term trends of student abilities across subjects

Long-term student achievement data provide useful information to formula...
research
07/30/2019

Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis

In this paper, in following of the first part (which ADF tests using ACI...

Please sign up or login with your details

Forgot password? Click here to reset