Predicting the future with goopti
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Transcript of Predicting the future with goopti
The problem/our business model
• Combine multiple orders into one vehicle (packing)
• Baseline prices assume certain occupancy
• Pricing as risk hedging
• Demand responsiveness
• Demand prediction price occupancy
order clumping
higher occupancy, lower risk
Predicting the future, part I: The Basics
Decompose +
Compose
Decompose +
Compose
Seasonality• Hour of day
• Day of week
• Week/month of year
• …
• de-trend + FFT
• A lot of constituent variables have seasonal component as well!
Trends
Feature engineering• 1st, 2nd derivates
• Scale-invariant encoding
• Distribution = binning + one-hot encoding
• Time series are a rich source of features (and inspiration)
• github.com/blue-yonder/tsfresh
Decompose +
Compose
Additive models
Additive = interpretable*
* sort of, maybe, sometimes
Not additive? No problem. Box-Cox transform to the
rescue.
Generalized Additive Models (GAM)
• Don’t have to choose model upfront
• Prescreen variables (weight of evidence) + multivariate selection (stepwise, shrinkage)
• Regularization via controlling smoothness
link function
smooth nonparametric
function
Simulations (aka.: It’s counting all the way down!)
1. Sample from distributions (can be completely empirical!)
2. Combine (can use arbitrary domain logic)
3. Repeat 1000s of times
4. Aggregate (mean + variance)
Feedback loopsWhen predicting a continues process, use errors from previous points in time as a corrective signal for subsequent predictions.
Cold start problems• Model transfer
• Switch/add models
• as available data increases
• when uncertainty too high
• Enrich data with outside sources
Predicting the future, part II: The Big Guns
EnsamblesCombining, stacking
LSTM
• Don't multiply, use addition instead
• Gate all operations so that you don't have to cram everything
(= input->hidden) (= hidden->output)
(= hidden->hidden)
Takeouts1. Decompose + compose
2. Additive = interpretable (and open to experiments)
3. Data science is about encoding and representing information, not algebra
4. Different models for different occasions
5. Going immediately for the big guns is an all-or-nothing proposition
Further reading
• multithreaded.stitchfix.com/assets/files/gam.pdf
• www.wessa.net/download/stl.pdf
• colah.github.io/posts/2015-08-Understanding-LSTMs
• github.com/blue-yonder/tsfresh
