Skip to main content
SpringerLink
Log in

A New Precipitable Water Vapor STARMA Model Based on Newton’s Method

  • Conference paper
  • First Online:
Fuzzy Systems & Operations Research and Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 367))

Abstract

The STARMA (space-time autoregressive moving average) model is introduced in 2002–2008 monthly PWV fitting and forecast. To enhance the model’s ability of dealing with satellite remote sensing raster data, this article extends the parameter estimation process in the STARMA model by augmenting the Newton’s method to high dimensions for solving system of nonlinear equations, and the process of parameter estimation is elaborated. This operation is validated by real data experiment results. The confirmation results of this method reveals that the STARMA model has good accuracy in both fitting and predicting.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cliff, A.D., Ord, J.K.: Space-time modelling with an application to regional forecasting. Trans. Inst. Br. Geogr. 64, 119–128 (1975)

    Article  Google Scholar 

  2. Martin, R.L., Oeppen, J.E.: The identification of regional forecasting models using space-time correlation functions. Trans. Inst. Br. Geogr. 66, 95–118 (1975)

    Article  Google Scholar 

  3. Aroian, L.A.: Time series in m-dimensions: definition, problems and prospects. Commun. Stat.-Simul. Comput. 5(B9), 453–465 (1980)

    Article  Google Scholar 

  4. Aroian, L.A.: Time series in m-dimensions: autoregressive models. Commun. Stat.-Simul. Comput. 5(B9), 491–513 (1980)

    Google Scholar 

  5. Oprian, C.A., Taneja, V.S., Voss, D.A., et al.: General considerations and interrelationships between MA and AR models, time series in m dimensions, the ARMA model. Commun. Stat.-Simul. Comput. 5(B9), 515–532 (1980)

    Article  Google Scholar 

  6. Voss, D.A., Oprian, C.A., Aroian, L.A.: Moving average models time series in m-dimensions. Commun. Stat.-Simul. Comput. 5(B9), 467–489 (1980)

    Article  Google Scholar 

  7. Pfeifer, P.E., Deutsch, S.J.: A comparison of estimation procedures for the parameters of the STAR model. Commun. Stat.-Simul. Comput. 3(B9), 255–270 (1980)

    Article  Google Scholar 

  8. Pfeifer, P.E., Deutsch, S.J.: Stationarity and invertibility regions for low order STARMA models. Commun. Stat.-Simul. Comput. 5(B9), 551–562 (1980)

    Article  Google Scholar 

  9. Epperson, B.K.: Spatial and space-time correlations in systems of subpopulations with genetic drift and migration. Genetics 133(3), 711–727 (1993)

    Google Scholar 

  10. Cressie, N., Majure, J.J.: Spatio-temporal statistical modeling of livestock waste in streams. J. Agric. Biol. Environ. Stat. 2(1), 24–47 (1997)

    Article  MathSciNet  Google Scholar 

  11. Giacomini, R., Granger, C.W.J.: Aggregation of space-time processes. J. Econometrics 118, 7–26 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  12. Sartoris, A.: STARMA Models for Crime in the City of Sao Paulo, Kiel, Germany. Kiel Institute for World Economics (2005)

    Google Scholar 

  13. Hernández-Murillo, R., Owyang, M.T.: The information content of regional employment data for forecasting aggregate conditions. Econ. Lett. 90(3), 335–339 (2006)

    Article  MATH  Google Scholar 

  14. Garrido, R.A.: Spatial interaction between the truck flows through the Mexico-Texas border. Transp. Res. A: Policy Pract. 34(1), 23–33 (2000)

    Google Scholar 

  15. Kamarianakis, Y., Prastacos, P.: Space-time modeling of traffic flow. Comput. Geosci. 31(2), 119–133 (2005)

    Article  Google Scholar 

  16. Bevis, M., Businger, S., Herring, T.A., et al.: GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res.: Atmos. 97(D14), 15787–15801 (1992)

    Article  Google Scholar 

  17. Crespo, J.L., Zorrilla, M., Bernardos, P., et al.: A new image prediction model based on spatio-temporal techniques. Visual Comput. 23(6), 419–431 (2007)

    Article  Google Scholar 

  18. Lee, C.: Space-Time Modeling and Application to Emerging Infectious Diseases. Michigan State University (2005)

    Google Scholar 

  19. Miu, W.J., Ming, D., Tao, C., et al.: Space and Time Series Data Analysis and Modeling. Science Press, Beijing (2012)

    Google Scholar 

  20. Yan, W.: Application of Time Series Analysis (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Firefighting and Rescue Technology, MPS, Langfang Hebei, 065000, China

    Zhihui Li

  2. Fire Engineering Department of Chinese People’s Armed Police Forces Academy, Langfang, 065000, China

    Zhihong Miao

Authors
  1. Zhihui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhihong Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhihui Li .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Bing-Yuan Cao

  2. Foreign Affairs Office, National Defense University, Beijing, China

    Zeng-Liang Liu

  3. Higher Education Mega Center, Guangzhou University, Guangzhou, China

    Yu-Bin Zhong

  4. Zhuhai Campus, Beijing Normal University, Zhuhai, China

    Hong-Hai Mi

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Li, Z., Miao, Z. (2016). A New Precipitable Water Vapor STARMA Model Based on Newton’s Method. In: Cao, BY., Liu, ZL., Zhong, YB., Mi, HH. (eds) Fuzzy Systems & Operations Research and Management. Advances in Intelligent Systems and Computing, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-319-19105-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19105-8_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19104-1

  • Online ISBN: 978-3-319-19105-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation