frequency domain predictive modeling with aggregated...
TRANSCRIPT
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Frequency Domain Predictive Modeling withAggregated Data
Sanmi Koyejo
University of Illinois at Urbana-Champaign
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Joint work with
Avradeep Bhowmik Joydeep Ghosh
@University of Texas at Austin
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Images courtesy: Econintersect (BEA), NOAA, Blue Hill Observatory
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Motivation
Data often released in aggregated form in practice (Burrellet al., 2004; Lozano et al., 2009; Davidson et al., 1978)
Worse, sampling periods need not be aligned, aggregationperiods need not be uniform1
ratio of government debt to GDP reported yearlyGDP growth rate reported quarterlyunemployment rate and ination rate reported monthlyinterest rate, stock market indices and currency exchange ratesreported daily
1Bureau of Labor Statistics, Bureau of Economic Analysis
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Motivation - II
Naive tting of aggregated data may result in ecologicalfallacy (Freedman et al., 1991; Robinson, 2009)
Reconstruction (before model tting) is expensive andunreliable
Main Contribution
Model estimation procedure in the frequency domain
avoids input data reconstruction
achieves provably bounded generalization error.
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Motivation - II
Naive tting of aggregated data may result in ecologicalfallacy (Freedman et al., 1991; Robinson, 2009)
Reconstruction (before model tting) is expensive andunreliable
Main Contribution
Model estimation procedure in the frequency domain
avoids input data reconstruction
achieves provably bounded generalization error.
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Problem Setup
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Features x(t) = [x1(t), x2(t) · · ·xd(t)], targets y(t)
Weak Stationarity+
Zero-mean E[y(t)] = 0.
Finite variance E[y(t)] <∞Autocorrelation function satises: E[y(t)y(t′)] = ρ(‖t− t′‖)
same assumptions for x(t)
Residual process
Let εβ(t) = x(t)>β − y(t) be the residual error process of alinear model
Observe that εβ(t) is weakly stationary
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Features x(t) = [x1(t), x2(t) · · ·xd(t)], targets y(t)
Weak Stationarity+
Zero-mean E[y(t)] = 0.
Finite variance E[y(t)] <∞Autocorrelation function satises: E[y(t)y(t′)] = ρ(‖t− t′‖)
same assumptions for x(t)
Residual process
Let εβ(t) = x(t)>β − y(t) be the residual error process of alinear model
Observe that εβ(t) is weakly stationary
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Problem Setup - II
Performance measure is the expected squared residual error
L(β) = E[|εβ(t)|2] = E[|x(t)>β − y(t)|2]
which is optimized as:
β∗ = arg minβ
L(β)
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Data Aggregation in Time Series
Non-Aggregated Feature X1
Aggregated Feature X1
Non-Aggregated Feature X2
Aggregated Feature X2
Non-Aggregated Feature X3
Aggregated Feature X3
Non-Aggregated Target YAggregated Target Y
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Data Aggregation in Time Series - II
Each coordinate of the feature set is aggregated
xi[l] =1
Ti
lTi/2∫(l−1)Ti/2
xi(τ)dτ
Similarly, the targets are aggregated
y[k] =1
T
kT/2∫(k−1)T/2
y(τ)dτ
for k, l ∈ Z = · · · − 1, 0, 1, · · · .
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Aggregation in Time and Frequency DomainFourier captures global properties of the signal
In time domain, convolution with square wave + sampling
z(t)convolution−−−−−−−→ sampling−−−−−−→ −→ z[k]
In frequency domain, multiplication with sinc function + sampling
Z(ω)multiplication−−−−−−−−−→ sampling−−−−−−→ −→ Z(ω)
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Restricted Fourier Transform
For signal z(t), T -restricted Fourier Transform dened as:
ZT (ω) = FT [z](ω) =
∫ T
−Tz(t)e−ιωtdt
Equivalent to a full Fourier Transform if the signal istime-limited within (−T, T )
Always exists nitely if the signal z(t) is nite
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Time-limited Data
Innite time series data are not available, instead assume dataavailable between time intervals (−T0, T0)
We apply T0-restricted Fourier transforms computed fromtime-limited data
Assume time-restricted Fourier transform decay rapidly withfrequency e.g. autocorrelation function is a Schwartzfunction (Terzioglu, 1969)
Thus, most of the signal power between frequencies (−ω0, ω0)
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Proposed Algorithm
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Step I
1 Input parameters T0, ω0, D, aggregated data samples x[k],y[l]
2 Sample D frequencies uniformly between (−ω0, ω0)
Ω = ω1, ω2, · · ·ωD : ωi ∈ (−ω0.ω0)
3 For each ω ∈ Ω, compute T0-restricted Fourier TransformsXT0(ω),YT0(ω) from aggregated signals x[k],y[l]
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Step I
1 Input parameters T0, ω0, D, aggregated data samples x[k],y[l]
2 Sample D frequencies uniformly between (−ω0, ω0)
Ω = ω1, ω2, · · ·ωD : ωi ∈ (−ω0.ω0)
3 For each ω ∈ Ω, compute T0-restricted Fourier TransformsXT0(ω),YT0(ω) from aggregated signals x[k],y[l]
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Step I
1 Input parameters T0, ω0, D, aggregated data samples x[k],y[l]
2 Sample D frequencies uniformly between (−ω0, ω0)
Ω = ω1, ω2, · · ·ωD : ωi ∈ (−ω0.ω0)
3 For each ω ∈ Ω, compute T0-restricted Fourier TransformsXT0(ω),YT0(ω) from aggregated signals x[k],y[l]
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Step II
Recall: UT is Fourier transform of square wave
4 Estimate non-aggregated Fourier transforms
Xi,T0(ω) =Xi,T0(ω)
UTi(ω), YT0(ω) =
YT0(ω)
UT (ω)
5 Estimate parameter β as:
β = arg minβ
1
|Ω|∑ω∈Ω
E‖XT0(ω)>β − YT0(ω)‖2
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Step II
Recall: UT is Fourier transform of square wave
4 Estimate non-aggregated Fourier transforms
Xi,T0(ω) =Xi,T0(ω)
UTi(ω), YT0(ω) =
YT0(ω)
UT (ω)
5 Estimate parameter β as:
β = arg minβ
1
|Ω|∑ω∈Ω
E‖XT0(ω)>β − YT0(ω)‖2
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Generalization Analysis
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Main result I
Theorem (Bhowmik, Ghosh, and Koyejo (2017))
For every small ξ > 0, ∃ corresponding T0, D such that:
E[|x(t)>β − y(t)|2
]< (1 + ξ)
(E[|x(t)>β∗ − y(t)|2
])+ 2ξ
with probability at least 1− e−O(D2ξ2)
Thus, generalization error bounded with suciently large T0, D
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Aliasing Eects, Non-uniform Sampling
Signals not bandlimited ⇒ Aliasing
Errors minimum for frequencies around 0
=⇒
Non-uniform sampling leads to further error
Performance will depend on rapid decay of power spectraldensity
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Aliasing Eects, Non-uniform Sampling
Signals not bandlimited ⇒ Aliasing
Errors minimum for frequencies around 0
=⇒
Non-uniform sampling leads to further error
Performance will depend on rapid decay of power spectraldensity
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Main result IINon-uniform aggregation, Finite samples
Theorem (Bhowmik, Ghosh, and Koyejo (2017))
Let ωi, ωy be the sampling rate for xi(t), y(t) respectively. Let
ωs = minωy, ω1, ω2, · · ·ωd. Then, for small ξ > 0, ∃corresponding T0, D such that:
E[|x(t)>β − y(t)|2
]<(1 + ξ)
(E[|x(t)>β∗ − y(t)|2
])+4ξ + 2e−O((ωs−2ω0)2)
with probability at least 1− e−O(D2ξ2) − e−O(N2ξ2)
Generalization error can be made small if T0, D are high, ω0 is small,
minimum sampling frequency ωs is high
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Empirical Evaluation
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Synthetic Data
Fig 1(a): No Discrepancy Fig 1(b): Low Discrepancy
Performance on synthetic data with varying ω0, and increasingsampling and aggregation discrepancy
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Synthetic Data - II
Fig 1(c): Medium Discrepancy Fig 1(d): High Discrepancy
Performance on synthetic data with varying ω0, and increasingsampling and aggregation discrepancy
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Las Rosas Dataset
Regressing corn yield against nitrogen levels, topographicalproperties, brightness value, etc.
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UCI Forest Fires Dataset
Regressing burned acreage against meteorological features, relativehumidity, ISI index, etc. on UCI Forest Fires Dataset
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Comprehensive Climate Dataset (CCDS)
Regressing atmospheric vapour levels over continental UnitedStates vs readings of carbon dioxide levels, methane, cloud cover,
and other extra-meteorological measurements
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Conclusion
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Additional Details
More detailed analysis (not shown) allows for more preciseerror control
Algorithm and analysis easily extend to multi-dimensionalindexes e.g. spatio-temporal data using the multi-dimensionalFourier transform
number of frequency samples may depend exponentially onindex dimension (typically < 4)
Extends to cases where aggregation and sampling period arenon-overlapping.
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Conclusion and Future Work
Proposed a novel procedure with bounded generalization errorfor learning with aggregated data
Signicant improvements vs reconstruction-based estimation.
Future Work:
Exploit other frequency domain structure e.g. sparse spectrumto improve estimates.
Extensions to non-linear estimators
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Conclusion and Future Work
Proposed a novel procedure with bounded generalization errorfor learning with aggregated data
Signicant improvements vs reconstruction-based estimation.
Future Work:
Exploit other frequency domain structure e.g. sparse spectrumto improve estimates.
Extensions to non-linear estimators
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Questions?
For more details:
Bhowmik, A., Ghosh, J. and Koyejo, O., 2017. Frequency Domain
Predictive Modeling with Aggregated Data. In Proceedings of the20th International conference on Articial Intelligence and Statistics(AISTATS).
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References
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References I
Avradeep Bhowmik, Joydeep Ghosh, and Oluwasanmi Koyejo. Frequency domain predictive modellingwith aggregated data. In Proceedings of the 20th International conference on Articial Intelligenceand Statistics (AISTATS), 2017.
Jenna Burrell, Tim Brooke, and Richard Beckwith. Vineyard computing: Sensor networks in agriculturalproduction. IEEE Pervasive computing, 3(1):3845, 2004.
James EH Davidson, David F Hendry, Frank Srba, and Stephen Yeo. Econometric modelling of theaggregate time-series relationship between consumers' expenditure and income in the unitedkingdom. The Economic Journal, pages 661692, 1978.
David A Freedman, Stephen P Klein, Jerome Sacks, Charles A Smyth, and Charles G Everett.Ecological regression and voting rights. Evaluation Review, 15(6):673711, 1991.
Aurelie C Lozano, Hongfei Li, Alexandru Niculescu-Mizil, Yan Liu, Claudia Perlich, Jonathan Hosking,and Naoki Abe. Spatial-temporal causal modeling for climate change attribution. In Proceedings ofthe 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages587596. ACM, 2009.
William S Robinson. Ecological correlations and the behavior of individuals. International journal ofepidemiology, 38(2):337341, 2009.
T Terzioglu. On schwartz spaces. Mathematische Annalen, 182(3):236242, 1969.