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Mining Airfare Data to Minimize Ticket Purchase Price
Oren Etzioni (UW) Craig Knoblock (USC) Alex Yates (UW) Rattapoom Tuchinda (USC)
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Etzioni, UW 2
Price change over time for American Airlines flight #192:223, LAX-BOS, departing on Jan. 2.
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Etzioni, UW 3
Consumers’ Dilemma
To Buy or Not to Buy…that is the question..
Data mining à Price drops
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Etzioni, UW 4
Advisor Model
1. Consumer wants to buy a ticket. 2. Hamlet: ‘buy’ (this is a good price). 3. Or: ‘wait’ (a better price will emerge). 4. Notify consumer when price drops.
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Etzioni, UW 5
Arbitrage Model
1. “going price” is $900. 2. Hamlet anticipates a price of $400. 3. Hamlet offers a $600 fare. 4. Hamlet buys when the price drops to $400. 5. Consumer saves $300; Hamlet earns $200. (of course, Hamlet could lose money!)
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Etzioni, UW 6
Will Flights sell out?
1. Watch the number of empty seats. 2. Upgrade to business class. 3. Place on another flight and give a free ticket.
In our experiment: upgrades were sufficient.
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Etzioni, UW 7
Is Airfare Prediction Possible???
" Complex “yield management” algorithms. - airlines have tons of historical data.
" Exogenous events create randomness.
How about the stock market? " True markets are unpredictable. " For Hamlet, prices are set by the airlines!
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Etzioni, UW 8
Surprising Experimental Result
Savings: buy immediately versus Hamlet. Optimal: buy at the best possible time.
Though it be madness, yet there be method in it.
HAMLET’s savings were 61.8% of optimal!
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Data Set
" Used Fetch.com’s data collection infrastructure. " Collected over 12,000 price observations:
– Lowest available fare for a one-week roundtrip. – LAX-BOS and SEA-IAD. – 6 airlines including American, United, etc. – 21 days before each flight, every 3 hours.
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Etzioni, UW 10
Learning Task Formulation
Input: price observation data. Algorithm: label observations (decision point);
run learner. Output: Classify each decision point à
buy versus wait.
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Etzioni, UW 11
Formulation Fine Points
" Want to learn from the latest data. " Run learner nightly to produce a new model.
– Learner is trained on data gathered to date. " Learned policy is a sequence of 21 models.
" Test set: 8 * 21 decision points for the last 1/3 of the flights.
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Labeling Training Data
IF price drops between and now THEN label(O)=wait ELSE label(O) à Pr(price will drop between now and takeoff)
takeoff now O
5 days 11 days
We estimate Pr based on behavior of past flights.
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Etzioni, UW 13
Candidate Approaches
" Fixed: “asap”, 14 days prior, 7 days,… " By hand: an expert looks at the data. " Time series:
– Not effective at price jumps! " Reinforcement learning: Q-learning.
– Used in computational finance. " Rule learning: Ripper, …
).,...,( 121 PPPFP ttt −−=
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Ripper
. THEN BOS-LAX route AND 2223 price AND 252 takeoff-before-hours IF
wait=
≥≥
• Features include price, airline, route, hours-before-takeoff, etc.
• Learned 20-30 rules…
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Etzioni, UW 15
Simple Time Series
" Predict price using a fixed window of k price observations weighted by α.
" We used a linearly increasing function for α
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Etzioni, UW 16
Q-learning
Natural fit to problem
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Etzioni, UW 17
Hamlet
" Stacking with three base learners: 1. Ripper (e.g., R=wait) 2. Time series 3. Q-learning (e.g., Q=buy)
" Ripper used as the meta-level learner. " Output: classifies each decision point as ‘buy’ or ‘wait’.
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Etzioni, UW 18
Experimental Results
" Real price data; Simulated passengers. – Uniform distribution over decision points. (sensitivity)
Requesting specific flights (also 3hr interval). " Learner run once per day on “past data”. " Execution: label each purchase point until buy
(or sell out). " Compute savings (or loss).
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Net Savings by Method
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
Savings by Method • Net savings = cost now – cost at purchase point. • Penalty for sell out = upgrade cost. 0.42% of the time. • Total ticket cost is $4,579,600.
-9.5%
3.4% 3.8% 3.8%
4.4%
7.0% Legend: Time Series Q-Learning By Hand Ripper Hamlet Optimal
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Etzioni, UW 20
Interval Savings
$0
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
$350,000
Sensitivity Analysis
" Passenger requests any nonstop flight in a 3 hour interval:
-5.7%
3.3% 3.6% 3.8% 4.2%
7.1% Legend: Time Series Q-Learning By Hand Ripper Hamlet Optimal
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Upgrade Penalty
Method Upgrade Cost % UpgradesOptimal $0 0%By hand $22,472 0.36%Ripper $33,340 0.45%Time Series $693,105 33.00%Q-learning $29,444 0.49%Hamlet $38,743 0.42%
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Discussion
" 76% of the time --- no savings possible. " Uniform distribution over 21 days. " 33% of the passengers arrived in the last week. " No passengers arrived >21 days before.
Simulation understates possible savings!
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Savings on “Feasible” Flights
Method Net SavingsOptimal 30.6%By hand 21.8%Ripper 20.1%Time Series 25.8%Q-learning 21.8%Hamlet 23.8%
Comparison of Net Savings (as a percent of total ticket price) on Feasible Flights
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Related Work
" Trading agent competition. – Auction strategies
" Temporal data mining. " Time Series. " Computational finance.
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Future Work
" More tests: international, multi-leg, hotels, etc. " Cost sensitive learning (tried MetaCost). " Additional base learners " Bagging/boosting " Refined predictions " Commercialization: patent, license.
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Conclusions
1. Dynamic pricing is prevalent. 2. Price mining a-la-Hamlet is feasible. 3. Price drops can be surprisingly predictable. 4. Need additional studies and algorithms. 5. Great potential to help consumers!
All’s well that ends well.
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Savings by Method
Method Savings Losses Upgrade Cost % Upgrades Net Savings % Savings % of OptimalOptimal $320,572 $0 $0 0% $320,572 7.0% 100.0%By hand $228,318 $35,329 $22,472 0.36% $170,517 3.8% 53.2%Ripper $211,031 $4,689 $33,340 0.45% $173,002 3.8% 54.0%Time Series $269,879 $6,138 $693,105 33.00% -$429,364 -9.5% -134.0%Q-learning $228,663 $46,873 $29,444 0.49% $152,364 3.4% 47.5%Hamlet $244,868 $8,051 $38,743 0.42% $198,074 4.4% 61.8%
• Savings over “buy now”. • Penalty for sell out = upgrade cost. • Total ticket cost is $4,579,600.
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Sensitivity Analysis
" Passenger requests any nonstop flight in a 3 hour interval:
Method Net Savings % of Optimal % upgradesOptimal $323,802 100.0% 0.0%By hand $163,523 55.5% 0.0%Ripper $173,234 53.5% 0.0%Time Series -$262,749 -81.1% 6.3%Q-Learning $149,587 46.2% 0.2%Hamlet $191,647 59.2% 0.1%
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Another Chart
Savings by Method
($500,000)
($400,000)($300,000)
($200,000)($100,000)
$0$100,000
$200,000$300,000
$400,000Ti
me
Ser
ies
Q-
lear
ning
By
hand
Rip
per
Ham
let
Opt
imal
Gross SavingsNet Savings