Future Prediction of Regional City based on Causal Inference using Time-series Data

Katsuhito Nakazawa, Tetsuyoshi Shiota, Tsutomu Tanaka

2016

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 practical and effective measure that originated from the social issue.

References

  1. Rubin, Donald, 1974. Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66 (5), 688-701.
  2. Pearl, Judea, 1985. Bayesian Networks: a Model of SelfActivated Memory for Evidential Reasoning. Proceedings, Cognitive Science Society, 329-334.
  3. S. Shimizu, A. Hyvärinen, Y. Kano, P. O. Hoyer, 2005. Discovery of non-gaussian linear causal models using ICA. In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI2005, 526- 533.
  4. Granger, C. W. J., 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37 (3), 424-438.
  5. Ministry of Internal Affairs and Communications, 2016. eStat: Portal site of official statistics of Japan. http://www.e-sat.go.jp/SG1/estat/eStatTopPortalE.do.
  6. Rodgers, J. L., Nicewander, W. A., 1988. Thirteen ways to look at the correlation coefficient. The American Statistician, 42 (1), 59-66.
  7. Sims, Christopher A., 1980. Macroeconomics and Reality. Econometrica, 48, 1-48.
  8. National Institute of Population and Social Security Research, 2013. Regional population projections for Japan: 2010-2040, Population Research Series, 330.
Download


Paper Citation


in Harvard Style

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 - Volume 1: SIMULTECH, ISBN 978-989-758-199-1, pages 203-210. DOI: 10.5220/0005961902030210


in Bibtex Style

@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 - Volume 1: SIMULTECH,},
year={2016},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005961902030210},
isbn={978-989-758-199-1},
}


in EndNote Style

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