In general, predictive Information falls into two categories – projections and predictions.
At Iteris, one of the most common projections we deal with is climate data. We take past information and project that into the future. For example, looking at Figure 1, which plots surface air temperature, the white area represents past information while the yellow shaded area represents projections into the future. (As a side note, Iteris’ ClearAg Platform leverages historical weather data going back 35+ years, which combined with current conditions datasets provides a robust projection of the future).
Based on Figure 1, the surface air temperature for the specified location can range on average between a low of 54°F to a high of 79°F. With this information, users can anticipate the vegetation and water availability, which is important for seasonal planning.
As we move into predictive information, we rely more on current information to predict the future. At Iteris, the most common predictions we encounter are weather forecasts. By taking physics and dynamics equations, current information, and using prognostic forms of those physics and dynamics equations, we are able to predict into the future. In Figure 2, we’ve taken Figure 1 and plotted current observations that have occurred (blue bars).
While there were some days that were in fact above average and some days that were below, overall the plotted observations are generally encompassed by the green shaded area (climatological range). By implementing forecast models, we are then able to leverage this information for future predictions to give us some idea of what is most likely to occur.
However, as we identify projections and predictions, its important to note that they’re just datasets. The key to making data work for you and your business needs is identifying the patterns in the data. By doing so, users can leverage predictive analytics for decision support, product insight and planning, and even product development.
More information about predictive and prescriptive analytics can be found in our on-demand webinar from Part II of Analytics and Agriculture series.
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About the Author
Cindy Vuong is marketing manager, Agriculture and Weather Analytics at Iteris.
Connect with Cindy on LinkedIn.