The genus Cinnamomum

Economics and Marketing of Cinnamon and Cassia 307
provided and quantitatively present the future uncertainty. Lack of quality data forced
us to choose methodologies, which forecast the future by fitting quantitative models to
statistical patterns from historic data for several years. Therefore, univariated methodologies
based solely on the history of the variable (one at a time) were tried. There are
three such models:
Simple moving average models
Exponential smoothing models
Box-Jenkins models.
To identify the right model the data have been explored first.
Exploring the data
The time series data on production and export were plotted/graphed to select an
appropriate model. The characteristics observed in the time series data for cinnamon
are:
1. There is an overall positive trend (i.e. the trend cycle accounts for over 90%).
2. Non-seasonal in nature.
3. The time series is non-stationary in both mean and variance.
The classical decomposition of the time series data also revealed the fact that the
trend cycle accounted for about 95% and above, while the irregularity accounted for
the rest. Thus the forecasting model should account for trend, non-seasonality and also
the non-stationary factor. Though (Box-Jenkins) models can be used, the models of
exponential smoothing were more suitable, as these models were built upon clear-cut
features like level, trend and seasonality.
Model selection
In order to identify a suitable model, the data was subjected to autocorrelation and partial
autocorrelation analysis. The outcome of the analysis for both production and
export separately indicated that AR (auto regression) (1) model was the suitable one.
The AR (1) model is identical with exponential smoothing (Box and Jenkins, 1976).
Hence exponential smoothing models were selected and tried.
The exponential smoothing, as its name suggests, extracts the level, trend and seasonal
index by constructing smoothed estimates of these features, weighing recent data more
heavily. It adapts to changing structure, but minimises the effects of outliers and noises.
Three major exponential smoothing models are available:
a. Simple exponential smoothing
b. Holt exponential smoothing
c. Winters exponential smoothing
Finally, the Holt exponential smoothing model was selected as the best and the forecasting
was done for variables in production and export.