Abstract:
In time series analysis, an Autoregressive Integrated Moving Average
(ARIMA) modeling technique is used. When an ARIMA model includes
other time series as input variables, the model is referred to as an ARIMAX
model. The rainfall is the main factor which influences the production levels
of paddy which we shall refer to as intervention events. Some of the other
important factors are the harvested area, sown area, year of production and
Agro-mechanical techniques etc. used. Due to the unsettled situation for last
two decades in this district, the data source centers could not collect data in
this period to study the production level to implement developing programs
particularly rainfall data but harvested area, sown area of paddy yield are
available in this period. Although rainfall data is available for a large number
of metrological stations in Sri Lanka, Vavuniya district has not been included
in their list of stations. Metrological department has developed an advanced
substation for this district recently and therefore it was not possible to obtain
reliable rainfall data from 1979 to 2010 in Vavuniya district. More
sophisticated models could be developed using ARIMA and ARIMAX
modeling techniques for the production levels of paddy by using the most
correlated factors Sown and Harvested area of paddy production level. We
used ARIMAX (0, 1, 1) for Maha season and ARIMAX (2, 1, 0) for Yala
season with Sown and harvested areas as covariates. When comparing the
ARIMAX covariate model with the baseline univariate ARIMA models, we
found that inclusion of covariates improve the fit RMSE by 14.36% for
Maha season and 11.32% for the Yala season.
