loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Katsuhito Nakazawa ; Tetsuyoshi Shiota and Tsutomu Tanaka

Affiliation: Fujitsu Laboratories Ltd., Japan

Keyword(s): Causal Inference, Future Prediction, Time-series Data, Regional City, Social Issue.

Abstract: Regional cities in Japan have a lot of social issues. Various measures are being considered to solve these social issues, but it is difficult to ascertain and implement practical and effective measures to address them. In this study, we proposed a method for selecting indicators that have a causal relation to solve the social issues based on a causal inference. If there was a causal relation between two sets of time-series data, the slope of the approximation line of the time-shifted correlation coefficients at the base time returned a negative value. The causal inference was verified by using samples of time-series data and we constructed a network of the causal indicators based on the causal inference. In addition, we achieved future predictions via the vector autoregressive model using the network of causal indicators. The model was verified using the actual time-series data of the 87 regional cities. As a result, it was possible to simulate future predictions by introducing pract ical and effective measure that originated from the social issue. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.226.166.214

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Nakazawa, K.; Shiota, T. and Tanaka, T. (2016). Future Prediction of Regional City based on Causal Inference using Time-series Data. In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-199-1; ISSN 2184-2841, SciTePress, pages 203-210. DOI: 10.5220/0005961902030210

@conference{simultech16,
author={Katsuhito Nakazawa. and Tetsuyoshi Shiota. and Tsutomu Tanaka.},
title={Future Prediction of Regional City based on Causal Inference using Time-series Data},
booktitle={Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2016},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005961902030210},
isbn={978-989-758-199-1},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Future Prediction of Regional City based on Causal Inference using Time-series Data
SN - 978-989-758-199-1
IS - 2184-2841
AU - Nakazawa, K.
AU - Shiota, T.
AU - Tanaka, T.
PY - 2016
SP - 203
EP - 210
DO - 10.5220/0005961902030210
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic