Agroindustrial project analysi

THE MARKETING FACTOR 63TIME-SERIES ANALYSIS. Time-series methods relate sales to timerather than to causal factors that may underlie sales performance.They use historical data to identify and project past patterns andtrends. These methods involve fitting a curve to the data and includefree-hand, semi-average, least-squares, and trend-line projections.In projecting trends, analysts should note the seasonal, secular,cyclical, and random variations. This is particularly important foragroindustries, which often face considerable price variabilityboth seasonally and across years. Historical statistics can be adjustedto be more representative. For example, sales for a particularperiod can be estimated by using a moving average of precedingmonths. Time series can be separated into seasonal or cyclicaltrends. Similarly, data can be weighted differently through exponentialweighting-for example, by assigning higher weight toyears that are thought to be more representative of future trends.These methods all represent various kinds of moving averages andinclude simple, weighted, exponential smoothing, and the Box-Jenkins autoregressive moving average (which also employsweighting techniques). Decomposition techniques are also usedto dissect time-series data into constituent elements. These timeseriesmethods may be defined as follows:18* Free-hand projection. The analyst plots the historical timeseriesdata and projects them linearly.* Semi-average projection. The analyst divides the series in half,calculates the average of each, and connects the two averageson the graph.e Least-squares curve fitting. The analyst fits a curve to thetime-series data by minimizing the squared error between theactual observations and the estimated curve.* Mathematical trend curve projection. The analyst fits a knownmathematical curve (with established properties) to the timeseriesdata.i Simple moving average. The analyst weights past observationsby 1/n; as new observations are made, they replace older onesin the calculation of revised averages.* Weighted moving average. Same as in simple moving average,except that the analyst attaches different weight to differentobservations based on their expected predictions.18. Ibid.