EGADS: Olympic Scoring

EGADs <https://github.com/yahoo/egads> is an open source time series analysis module for Java for forecasting future time series behavior. A useful model in the library is the Olympic Scoring algorithm that takes the average of multiple periods spaced in time to compute an expected value. For example, it will align the timestamps for the past 5 Mondays, optionally remove outlier values (the smallest or largest) then average the results to predict will be observed next Monday.

It is a simple algorithm but works fairly well at handling “seasonal” trends for human behavior.

Fields for the config include:

Name	Data Type	Required	Description	Default	Example
baselineQuery	SemanticQuery	Required	A full Semantic query to fetch the training data. This allows for a different set of smoothing to feed into the algorithm while comparing against the observed data.	null
baselinePeriod	String	Required	A duration for how wide the traning period should be to match period-over-period. Typically 1h for hourly cycles or 1w for daily cycles.	null	1w
baselineNumPeriods	Numeric	Required	The number of periods to look back for training data. E.g. for 7 weeks of history, use 7.	0	7
baselineAggregator	String	Optional	An aggregator to use when merging the historical periods. It should almost always be avg but could be max or min.	avg	avg
excludeMax	Numeric	Optional	How many of the highest values to exclude from the baseline calculation.	0	1
excludeMin	Numeric	Optional	How many of the smallest values to exclude from the baseline calculation.	0	1