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Seasonal fractional integrated time series models for rainfall data in Nigeria

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Abstract

Rainfall variability, seasonality and extremity have a lot of consequences in planning and decision making of every sphere of human endeavour especially in Nigeria where majority of agricultural practices and planning is dependent on rainfed agriculture. For this reason, an extensive understanding of rainfall regime is an important prerequisite in such planning. We approach this work using time series approach. Seasonality and possibility of long-term dependence in rainfall data are considered, and these have significant effects in explaining the distribution of rainfall in each state of the six geopolitical zones of Nigeria. The estimated seasonal autoregressive fractionally integrated moving average (SARFIMA) model for each of the six rainfall zones was found to perform better in predicting rainfall distribution than the corresponding seasonal autoregressive moving average (SARMA) model in terms of minimum Akaike information criterion (AIC) and other model diagnostic measures.

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Authors and Affiliations

  1. Department of Statistics, University of Ibadan, Ibadan, Nigeria

    Olaoluwa S. Yaya

  2. Department of Geography, University of Ibadan, Ibadan, Nigeria

    Olutoyin A. Fashae

Authors
  1. Olaoluwa S. Yaya
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  2. Olutoyin A. Fashae
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Correspondence to Olaoluwa S. Yaya.

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Yaya, O.S., Fashae, O.A. Seasonal fractional integrated time series models for rainfall data in Nigeria. Theor Appl Climatol 120, 99–108 (2015). https://doi.org/10.1007/s00704-014-1153-8

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  • DOI: https://doi.org/10.1007/s00704-014-1153-8

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