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Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool

January 24, 2018 / Global
Featured image for Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool
Figure 1: In the sliding window backtesting model, a fixed-size training window (in black) slides over the entire history of a time series and is repeatedly tested against a forecasting window (in orange) with older data points dropped.
Figure 2: In the expanding window backtesting model, a training window (in black) expands over the entire history of a time series and is repeatedly tested against forecasting window (in orange) without dropping older data points.
Table 1: Time series forecasting algorithms tested using Omphalos include those traditionally applied in R, Go, and Python.
Figure 3: The Omphalos architecture takes in time series data and a user-defined configuration file incorporating specified parameters as input, and produces a comprehensive backtesting summary report as output.
Table 2: When comparing popular forecasting algorithms in R with AutoForecaster, we used daily completed trip counts from 20 cities as our input data and configured a sliding window backtesting procedure with training window size, forecasting window size, and sliding steps set at 189, 14, and 14, respectively.
Figure 4: A histogram of backtested errors when leveraging Auto-Forecaster for a given time series can help us understand how our algorithms are performing.
Roy Yang

Roy Yang

Roy Yang is a data scientist on Uber's Forecasting Platform team.

Posted by Roy Yang

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