Exploratory Analysis of a Large Collection of Time-Series Using Automatic Smoothing Techniques
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1 Exploratory Analysis of a Large Collection of Time-Series Using Automatic Smoothing Techniques Ravi Varadhan, Ganesh Subramaniam Johns Hopkins University AT&T Labs - Research 1 / 28
2 Introduction Goal: To extract summary measures and features from a large collection of time series. 1 Exploratory analysis (as opposed to inferential) 2 Hypothesis generation 3 Interesting (anomalous) time series 4 Common features among time series (e.g., critical points) Process to be as automatic as possible. 2 / 28
3 What do we mean by features? Scale of time series Mean value of function Values of derivatives Outliers Critical points Curvatures Signal/noise Others avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 3 / 28
4 How do we do this? Features are defined on smooth curves. What we have is discretely sampled observations. We need functional data techniques to recover underlying smooth function. y(t i ) = f (t i ) + ε i ; E(ε i ) = 0 Automatic bandwidth selection procedures (e.g., cross-validation, plug-in) avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 4 / 28
5 Challenge Optimal bandwidth selection is usually applied to the function. This may NOT be optimal for estimating derivatives. The relationship between optimal BWs for function estimation and derivative estimation is not clear. Here we evaluate 4 automatic smoothing techniques in terms of their accuracy for estimating functions and its first two derivatives via simulation studies. avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 5 / 28
6 Smoothing techniques considered for study Smoothing splines with gcv for bw selection (stats::smooth.spline). Penalized splines with REML estimate(semipar::spm). Local polynomial with plugin bw (KernSmooth::locpoly). Gasser-Muller kernel global plug-in bw (lokern::glkerns). 6 / 28
7 Simulation study design Regression function. (4 functions with different characteristics) Error distribution. (t distribution 5 df) Grid layout. (either uniform random or equally spaced) Noise level. (σ = 0.5, 1.2) avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 7 / 28
8 Regression Function Estimation MISE, Variance & Bias 2 Function SS SPM GLK LOC f 1 (x) = x + 2 exp( 400x 2 ), σ = 0.5, f 2 (x) = [1 + exp ( 10x)] 1, σ = 0.5, f 3 (x) = 10 exp( x/60) sin( 2π (x 10)) + sin( 2π (x 30)) σ = e f 4 (x) = sin(8πx 2 ), σ = 0.5, / 28
9 First Derivative Estimation MISE, Variance & Bias 2 First Derivative SS SPM GLK LOC f 1 (x) = x + 2 exp( 400x 2 ), σ = 0.5, f 2 (x) = [1 + exp ( 10x)] 1, σ = 0.5, f 3 (x) = 10 exp( x/60) sin( 2π (x 10)) + sin( 2π (x 30)) σ = f 4 (x) = sin(8πx 2 ), σ = 0.5, / 28
10 Second Derivative Estimation MISE, Variance & Bias 2 Second Derivative SS SPM GLK LOC f 1 (x) = x + 2 exp( 400x 2 ), σ = 0.5, f 2 (x) = [1 + exp ( 10x)] 1, σ = 0.5, 6.6e e f 3 (x) = 10 exp( x/60) sin( 2π (x 10)) + sin( 2π (x 30)) σ = e f 4 (x) = sin(8πx 2 ), σ = 0.5, / 28
11 Highlights Smoothing spline, with cross-validated optimal bandwidth, did poorly. Penalized splines, with REML penalty estimation, did well on smooth functions, and worse on functions with high frequency variations (high bias). Global plug-in bandwidth kernel methods, glkerns and locpoly generally did well (higher variance). glkerns seems to be a good choice for estimating lower-order derivatives. 11 / 28
12 Exploration of AT&T Time-Series Data. An R function to extract summary measures and features of a collection of time series. We demonstrate that with a large collection of time series data from AT&T. Over 1200 time-series with monthly MOU over a 3.5 year period. The data were transformed & scaled for proprietary reasons. avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 12 / 28
13 Univariate View of Features 13 / 28
14 A Biplot on Features Figure: PCA of features Data ts: 1205 ts: / 28
15 Another Biplot on Features Figure: PCA of features Data ts: 139 ts: 936 Next 15 / 28
16 Figure: PCA of features Data Back to PCA 16 / 28
17 Figure: PCA of features Data Back to PCA 17 / 28
18 Figure: PCA of features Data Back to PCA 18 / 28
19 Figure: PCA of features Data Back to PCA 19 / 28
20 Future Work Release package. Add more visualization. Further testing on real data. avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 20 / 28
21 THANK YOU! 21 / 28
22 Semiparametric Model Details Nonparametric regression models are used. Functional form of the models We consider a univariate scatterplot smoothing y i = f (x i ) + ɛ i where the (x i, y i ), 1 i n, are scatter plot data, ɛ i are zero mean random variables with variance σɛ 2 and f (x) = E(y x) is a smooth function. f is estimated using penalised spline smoothing using truncated polynomial basis functions. These involve f being modelled as a function of the form f (x) = β 0 + β 1 x + + βp x p K + u k (x x k ) p k=1 where u k are random coefficients u [u 1, u 2,..., u K ] T N(0, σ 2 u Ω 1/2 (Ω 1/2 ) T ), Ω [ x k x k 2p ] The mixed model representation of penalised spline smoothers allows for automatic fitting using the R linear mixed model function. Smoothing parameter selection is done using REML and ˆf (x) is obtained via best linear unbiased prediction. This class of penalised spline smoothers may also be expressed as ˆf = C(C T C + λ 2p D) 1 C T y where λ = σ2 u σɛ 2 is the smoothing parameter, and D ( 02x2 0 2xK 0 Kx2 (Ω 1/2 ) T Ω 1/2 ) C [1, x i,..., x m 1 x i i x k 2p ] avi Varadhan, Ganesh Subramaniam ( Johns Hopkins University EDA of Large AT&T Time Labs series - Research Data ) 22 / 28
23 Simulation Output: Integrated Mean Sq. error, Variance & Bias (for random interval) Function SS SPM GLK LOC f 1 (x) = x + 2 exp( 400x 2 ), σ = 0.5, (2.100) MISE = (0.026) MISE = (0.049) MISE = (0.028) MISE = (2.100) ivar +(0.0041) isb (0.026) ivar +(0.0000) isb (0.048) ivar +(0.0000) isb (0.028) ivar +(0.0000) isb f 2 (x) = [1 + exp 10x] 1, σ = 0.5, (1.30) MISE = (0.68) MISE = (0.31) MISE = (0.27) MISE = (1.30) ivar + (0.10) isb (0.21) ivar + (0.470) isb (0.25) ivar + (0.065) isb (0.22) ivar + (0.055) isb f 3 (x) = 0.3 exp( 4(x + 1) 2 ) exp(16(x 1) 2 ), σ = 0.4, (2.30) MISE = (2.20) ivar + (0.059) isb (0.48) MISE = (0.23) ivar + (0.260) isb (0.43) MISE = (0.34) ivar + (0.093) isb (0.36) MISE = (0.30) ivar + (0.060) isb f 4 (x) = sin(6x), σ = 4, (9.40) MISE = (0.63) MISE = (0.95) MISE = (0.89) MISE = (9.40) ivar + (0.0430) isb (0.63) ivar + (0.0000) isb (0.95) ivar + (0.0093) isb (0.89) ivar + (0.0078) isb f 5 (x) = a exp( bx) + k 1 sin( 2π T 1 (x 10) + k 2 sin( 2π T 2 (x 30)), σ = 0.5, ( ) MISE = ( ) ivar + (5.4e 05) isb ( ) MISE = ( ) ivar + (2.1e 02) isb ( ) MISE = ( ) ivar + (1.3e 04) isb ( ) MISE = ( ) ivar + (2.5e 04) isb f 6 (x) = sin(8πx 2 ), σ = 0.5, (0.048) MISE = (0.640) MISE = (0.068) MISE = (0.089) MISE = (0.043) ivar +(0.0091) isb (0.120) ivar +(0.5200) isb (0.042) ivar +(0.0270) isb (0.027) ivar +(0.0620) isb 23 / 28
24 Simulation Output: Integrated Mean Sq. error, Variance & Bias (for random interval) First Derivative SS SPM GLK LOC f 1 (x) = x + 2 exp( 400x 2 ), σ = 1.6, (44.00) MISE = (0.80) MISE = (0.47) MISE = (0.66) MISE = (44.00) ivar + (0.21) isb (0.11) ivar + (0.69) isb (0.16) ivar + (0.30) isb (0.28) ivar + (0.38) isb f 2 (x) = [1 + exp 10x] 1, σ = 1.2, ( ) MISE = ( ) ivar + (6.300) isb (0.67) MISE = (0.57) ivar + (0.098) isb (3.20) MISE = (3.20) ivar + (0.014) isb (2.90) MISE = (2.90) ivar + (0.018) isb f 3 (x) = 0.3 exp( 4(x + 1) 2 ) exp(16(x 1) 2 ), σ = 0.4, (490.00) MISE = (1.00) MISE = (0.95) MISE = (1.50) MISE = (490.00) ivar + (0.52) isb (0.26) ivar + (0.75) isb (0.62) ivar + (0.33) isb (1.20) ivar + (0.23) isb f 4 (x) = sin(6x), σ = 4, ( ) MISE = ( ) ivar + (20.000) isb f 5 (x) = a exp( bx) + k 1 sin( 2π (x 10) + k T 2 sin( 2π 1 T 2 (x 30)), σ = (25.000) MISE = ( ) ivar + 0.5, (0.047) isb (5.3) MISE = (5.2) ivar + (0.086) isb (26.0) MISE = (26.0) ivar + (0.033) isb (40.0) MISE = (40.0) ivar + (0.048) isb (0.970) MISE = (0.0023) ivar +(0.970) isb (0.055) MISE = (0.0400) ivar +(0.015) isb (0.090) MISE = (0.0820) ivar +(0.008) isb f 6 (x) = sin(8πx 2 ), σ = 0.5, (0.13) MISE = (0.73) MISE = (0.17) MISE = (0.15) MISE = (0.098) ivar + (0.037) isb (0.130) ivar + (0.610) isb (0.041) ivar + (0.130) isb (0.047) ivar + (0.110) isb 24 / 28
25 Simulation Output: Integrated Mean Sq. error, Variance & Bias (for random interval) Second Derivative SS SPM GLK LOC f 1 (x) = x + 2 exp( 400x 2 ), σ = 1.6, (230.00) MISE = (1.00) MISE = (0.99) MISE = (1.00) MISE = (230.00) ivar + (1.00) isb (0.001) ivar + (1.00) isb (0.015) ivar + (0.97) isb (0.079) ivar + (0.96) isb f 2 (x) = [1 + exp 10x] 1, σ = 1.2, (6.6e + 06) MISE = (6.6e + 06) ivar + ( ) isb f 3 (x) = 0.3 exp( 4(x + 1) 2 ) exp(16(x 1) 2 ), σ = 0.4, (1.4e + 05) MISE = (1.4e + 05) ivar + (95.00) isb f 4 (x) = sin(6x), σ = 4, (3.7e + 10) MISE = (3.7e + 10) ivar + (1.4e + 07) isb f 5 (x) = a exp( bx) + k 1 sin( 2π (x 10) + k T 2 sin( 2π 1 T 2 (x 30)), σ = ( ) MISE = (4.6e + 03) + ivar 0.5, (7.800) isb (6.9e + 00) MISE = (4.8e +02) ivar +(3.6) isb (4.8e + 02) MISE = (2.2e + 02) MISE = (1.1e + 00) MISE = (1.5e 01) ivar + (0.94) isb (6.5e + 01) MISE = (6.5e +01) ivar +(6.6e 01) isb (1.8e + 00) MISE = (1.2e + 00) ivar + (0.62) isb (1.0e + 03) MISE = (1.0e + 03) ivar + (1.0e + 00) isb (3.7e + 01) MISE = (3.7e + 01) ivar + (0.44) isb (3.4e + 04) MISE = (3.4e + 04) ivar + (3.2e + 01) isb (1.00) MISE = (1.5e 03) ivar + (1.000) isb (0.231.) MISE = (1e 01) ivar + (0.120) isb (2.50) MISE = (2.5e + 00) ivar + (0.019) isb f 6 (x) = sin(8πx 2 ), σ = 0.5, (0.81) MISE = (0.80) MISE = (0.32) MISE = (0.41) MISE = (0.730) ivar + (0.084) isb (0.160) ivar + (0.640) isb (0.035) ivar + (0.290) isb (0.280) ivar + (0.130) isb 25 / 28
26 Simulation Output: Integrated Mean Sq. error, Variance & Bias (for random interval) Function SS SPM GLK LOC f 1 (x) = x + 2 exp( 16x 2 ), σ = 0.4, (0.083) MISE = (0.031) MISE = (0.022) MISE = (0.021) MISE = (0.080) ivar +(0.0029) isb (0.015) ivar +(0.0160) isb (0.017) ivar +(0.0043) isb (0.014) ivar +(0.0071) isb f 2 (x) = sin(2πx) + 2 exp( 16x 2 ), σ = 0.3, (0.092) MISE = (0.079) MISE = (0.035) MISE = (0.033) MISE = (0.089) ivar +(0.0034) isb (0.046) ivar +(0.0320) isb (0.026) ivar +(0.0091) isb (0.023) ivar + (0.100) isb f 3 (x) = 0.3 exp( 4(x + 1) 2 ) exp(16(x 1) 2 ), σ = 0.1, f 4 (x) = sin(6x), σ = 1, (0.600) MISE = (0.600) ivar + ( ) isb f 5 (x) = a exp( bx) + k 1 sin( 2π T 1 (x 10) + k 2 sin( 2π T 2 (x 30)), σ = 0.5, (0.160) MISE = (0.150) ivar + (0.000) isb (0.055) MISE = (0.049) ivar + (0.012) isb (0.051) MISE = (0.050) ivar + (0.000) isb (0.050) MISE = (0.050) ivar + (0.001) isb ( e 07) MISE = ( e 07) ivar + ( e 08) isb (0.041) MISE = (0.039) ivar + ( ) isb ( e 07) MISE = ( e 079) ivar + ( e 0) isb (0.078) MISE = (0.073) ivar + ( ) isb ( e 07) MISE = ( e 07) ivar + ( e 08) isb (0.064) MISE = (0.060) ivar + ( ) isb ( e 07) MISE = ( e 07) ivar + ( e 08) isb 26 / 28
27 Simulation Output: Integrated Mean Sq. error, Variance & Bias (for random interval) First Derivative SS SPM GLK LOC f 1 (x) = x + 2 exp( 16x 2 ), σ = 0.4, (55.00) MISE = (0.36) MISE = (0.27) MISE = (0.38) MISE = (55.00) ivar + (0.086) isb (0.12) ivar + (0.240) isb (0.15) ivar + (0.120) isb (0.28) ivar + (0.099) isb f 2 (x) = sin(2πx) + 2 exp( 16x 2 ), σ = 0.3, (8.0) MISE = (0.13) MISE = (0.08) MISE = (0.19) MISE = (8.000) ivar + (0.013) isb (0.071) ivar + (0.055) isb (0.048) ivar + (0.032) isb (0.170) ivar + (0.013) isb f 3 (x) = 0.3 exp( 4(x + 1) 2 ) exp(16(x 1) 2 ), σ = 0.1, (31.00) MISE = (0.24) MISE = (0.20) MISE = (0.32) MISE = (31.00) ivar + (0.049) isb (0.10) ivar + (0.140) isb (0.12) ivar + (0.084) isb (0.27) ivar + (0.048) isb f 4 (x) = sin(6x), σ = 1, ( ) MISE = ( ) ivar + (1.3000) isb f 5 (x) = a exp( bx) + k 1 sin( 2π (x 10) + k T 2 sin( 2π 1 T 2 (x 30)), σ = ( ) MISE = ( ) ivar + 0.5, ( ) isb (0.41) MISE = (0.34) ivar + (0.0750) isb (1.80) MISE = (1.80) ivar + (0.0087) isb (2.80) MISE = (2.80) ivar + (0.0077) isb ( ) MISE = ( ) ivar + ( ) isb ( ) MISE = ( ) ivar + ( ) isb ( ) MISE = ( ) ivar + ( ) isb 27 / 28
28 Simulation Output: Integrated Mean Sq. error, Variance & Bias (for random interval) Second Derivative SS SPM GLK LOC f 1 (x) = x + 2 exp( 16x 2 ), σ = 0.4, 1.8e e e 01.0e (+04) e e f 2 (x) = sin(2πx) + 2 exp( 16x 2 ), σ = 0.3, ( ) MISE = (2.4e + 03) + ivar (1.60) isb (0.24) MISE = (1.1e 01) ivar + (0.13) isb (0.24) MISE = (8.3e 02) ivar + (0.16) isb (12.00) MISE = (7.9e + 02) ivar + (0.88) isb f 3 (x) = 0.3 exp( 4(x + 1) 2 ) exp(16(x 1) 2 ), σ = 0.1, ( ) MISE = (0.52) MISE = (0.51) MISE = (15.00) MISE = ( ) ivar +(6.00) isb (0.16) ivar + (0.35) isb (0.19) ivar + (0.32) isb (14.00) ivar + (0.12) isb f 4 (x) = sin(6x), σ = 1, (2.3e + 09) MISE = (2.3e + 09) ivar + (8.7e + 05) isb f 5 (x) = a exp( bx) + k 1 sin( 2π (x 10) + k T 2 sin( 2π 1 T 2 (x 30)), σ = ( ) MISE = ( ) ivar + 0.5, ( ) isb (4.6e + 00) MISE = (4.1e +00) ivar +(5.4e 01) isb ( ) MISE = ( ) ivar + ( ) isb (6.5e + 01) MISE = (6.5e +01) ivar +(1.2e 01) isb ( ) MISE = ( ) ivar + ( ) isb (2.1e + 03) MISE = (2.1e + 03) ivar + (2.1e + 00) isb ( ) MISE = ( ) ivar + ( ) isb 28 / 28
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