Forecasting Multiple Time Series Using the baselineforecast R Package

1. Forecasting (Revenue for S&P 500 Companies) Using the baselineforecast Package by Konstantin Golyaev Microsoft Azure Machine Learning Konstantin Golyaev, useR! 2016, Stanford, CA 16/30/2016

2. Motivation • “Prediction is very difficult, especially about the future” • © Niels Bohr (allegedly) • We want to: • Forecast multiple time series at different horizons • Leverage useful external information, when available • Employ state-of-the-art methods Note: won’t show any results due to five-minute time constraint  Konstantin Golyaev, useR! 2016, Stanford, CA 26/30/2016

3. Two Ways to Forecast 1. Time-series methods (ARIMA, ETS, STL, etc.) • Great for modeling trend and seasonality 2. Regression-based methods (elastic net, random forest, boosted regression trees, etc.) • Derive power from external information (features) Can we get the best of both worlds? Konstantin Golyaev, useR! 2016, Stanford, CA 36/30/2016

4. Konstantin Golyaev, useR! 2016, Stanford, CA 46/30/2016

5. Illustration • Take small window of series 𝑦1 𝑦2 𝑦3 𝑦4 𝑦5 𝑦6 𝑦7 𝑦8 ⋮ Konstantin Golyaev, useR! 2016, Stanford, CA 56/30/2016

6. Illustration • Take small window of series • Fit a model to it, make forecasts few steps ahead 𝑦1 𝑦2 𝑦3 𝑦4 𝑦5 𝑦6 𝑦7 𝑦8 ⋮ 𝑓7|6 𝑓8|6 ⋮ Konstantin Golyaev, useR! 2016, Stanford, CA 66/30/2016

7. Illustration • Take small window of series • Fit a model to it, make forecasts few steps ahead • Move the window forward 𝑦1 𝑦2 𝑦3 𝑦4 𝑦5 𝑦6 𝑦7 𝑦8 ⋮ Konstantin Golyaev, useR! 2016, Stanford, CA 76/30/2016

8. Illustration • Take small window of series • Fit a model to it, make forecasts few steps ahead • Move the window forward • Repeat the process 𝑦1 𝑦2 𝑦3 𝑦4 𝑦5 𝑦6 𝑦7 𝑦8 ⋮ 𝑓8|7 𝑓9|7 ⋮ Konstantin Golyaev, useR! 2016, Stanford, CA 86/30/2016

9. Illustration • Take small window of series • Fit a model to it, make forecasts few steps ahead • Move the window forward • Repeat the process • Continue until out of data, combine results when done 𝑦7 𝑓7|6 𝑦8 𝑓8|6 𝑦8 𝑦9 ⋮ 𝑓8|7 𝑓9|7 ⋮ Konstantin Golyaev, useR! 2016, Stanford, CA 96/30/2016

10. What Else Can We Do? Konstantin Golyaev, useR! 2016, Stanford, CA 106/30/2016

11. Date-Based Features Examples: • Year • Quarter • Month • Week • Holidays • Etc… Konstantin Golyaev, useR! 2016, Stanford, CA 116/30/2016

12. Lags or Other Functions of 𝑦𝑡 • R does not compute lags correctly when series has gaps in its index (e.g. missing months/days) • So we implemented it Konstantin Golyaev, useR! 2016, Stanford, CA 126/30/2016

13. External Series as Features • This is very much problem-specific • What we used in various projects: • Macroeconomic data from Federal Reserve Economic Data (FRED) • Web search trends from Bing/Google/etc • Tweets scored for sentiments • External business drivers such as promotions Konstantin Golyaev, useR! 2016, Stanford, CA 136/30/2016

14. Implementation • All code is combined into baselineforecast R package • Function ConstructDataset() takes series 𝑦𝑡 and external data 𝑋𝑡, returns data frame with target and features • Function FitModel() interfaces with caret package to train any regression learning algorithm and perform time series cross-validation Konstantin Golyaev, useR! 2016, Stanford, CA 146/30/2016

15. Future Work • Exploratory Data Analysis • Computing Prediction Intervals • Decide on the license/distribution model Have questions? Ping me at Konstantin.Golyaev@Microsoft.com Konstantin Golyaev, useR! 2016, Stanford, CA 156/30/2016

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