Time Series
Karanveer Mohan
Keegan Go
EE103
Stanford University
November 12, 2015
Stephen Boyd
Outline
Introduction
Linear operations
Least-squares
Prediction
Introduction
2
Time series data
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represent time series x1 , . . . , xT as T -vector x
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xt is value of some quantity at time (period, epoch) t, t = 1, . . . , T
examples:
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–
–
–
–
–
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average temperature at some location on day t
closing price of some stock on (trading) day t
hourly number of users on a website
altitude of an airplane every 10 seconds
enrollment in a class every quarter
vector time series: xt is an n-vector; can represent as T × n matrix
Introduction
3
Types of time series
time series can be
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smoothly varying or more wiggly and random
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roughly periodic (e.g., hourly temperature)
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growing or shrinking (or both)
random but roughly continuous
(these are vague labels)
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Introduction
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Melbourne temperature
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daily measurements, for 10 years
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you can see seasonal (yearly) periodicity
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0
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0
Introduction
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Melbourne temperature
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zoomed to one year
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0
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Introduction
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Apple stock price
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log10 of Apple daily share price, over 30 years, 250 trading days/year
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you can see (not steady) growth
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1
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−1
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Introduction
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Log price of Apple
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zoomed to one year
0.85
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Introduction
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Electricity usage in (one region of) Texas
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total in 15 minute intervals, over 1 year
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you can see variation over year
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x 10
Introduction
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Electricity usage in (one region of) Texas
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zoomed to 1 month
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you can see daily periodicity and weekend/weekday variation
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Introduction
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Outline
Introduction
Linear operations
Least-squares
Prediction
Linear operations
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Down-sampling
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k× down-sampled time series selects every kth entry of x
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can be written as y = Ax
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for 2× down-sampling,

1 0
 0 0

 0 0

A= . .
 .. ..

 0 0
0 0
Linear operations
T even,
0
0
0
..
.
···
···
···
0
0
0
..
.
0
0
0
..
.
0
0
0
..
.
0 0 0 0
0 0 0 0
···
···
1
0
0
0
0
1
0
1
0
..
.
0
0
0
..
.
0
0
1
..
.
0
0
0
..
.








0 
0
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Down-sampling on Apple log price
4× down-sample
0.17
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0.1
0.09
5100
Linear operations
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Up-sampling
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k× (linear) up-sampling interpolates between entries of x
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can be written as y = Ax
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for 2× up-sampling

1
 1/2






A=






Linear operations

1/2
1
1/2
1/2
1
..
.
1
1/2













1/2 
1
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Up-sampling on Apple log price
4× up-sample
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Linear operations
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Smoothing
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k-long moving average y of x is given by
yi =
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1
(xi + xi+1 + · · · + xi+k−1 ),
k
can express as y = Ax,

1/3 1/3

1/3


A=


i = 1, . . . , T − k + 1
e.g., for k = 3,

1/3
1/3 1/3
1/3 1/3 1/3
..
.






1/3
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1/3
1/3
can also have trailing or centered smoothing
Linear operations
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Melbourne daily temperature smoothed
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centered smoothing with window size 41
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Linear operations
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First-order differences
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(first-order) difference between adjacent entries
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discrete analog of derivative
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express as y = Dx, D is the (T − 1) × T difference matrix


−1 1
...


−1 1 . . .


D=

.
.
..
..


. . . −1 1
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kDxk2 is a measure of the wiggliness of x
kDxk2 = (x2 − x1 )2 + · · · + (xT − xT −1 )2
Linear operations
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Outline
Introduction
Linear operations
Least-squares
Prediction
Least-squares
19
De-meaning
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de-meaning a time series means subtracting its mean:
x̃ = x − avg(x)
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rms(x̃) = std(x)
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this is the least-squares fit with a constant
Least-squares
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Straight-line fit and de-trending
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fit data (1, x1 ), . . . , (T, xT ) with affine model xt ≈ a + bt
(also called straight-line fit)
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b is called the trend
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a + bt is called the trend line
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de-trending a time series means subtracting its straight-line fit
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de-trended time series shows variations above and below the
straight-line fit
Least-squares
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Straight-line fit on Apple log price
Trend
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Residual
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Least-squares
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Periodic time series
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let P -vector z be one period of periodic time series
xper = (z, z, . . . , z)
(we assume T is a multiple of P )
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express as xper = Az with


IP


A =  ... 
IP
Least-squares
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Extracting a periodic component
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given (non-periodic) time series x, choose z to minimize kx − Azk2
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gives best least-squares fit with periodic time series
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simple solution: average periods of original:
ẑ = (1/k)AT x,
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k = T /P
e.g., to get ẑ for January 9, average all xi ’s with date January 9
Least-squares
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Periodic component of Melbourne temperature
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Least-squares
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Extracting a periodic component with smoothing
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can add smoothing to periodic fit by minimizing
kx − Azk2 + λkDzk2
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λ > 0 is smoothing parameter
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D is P × P circular difference matrix

−1 1

−1 1


.. ..
D=
.
.


1
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
−1





1 
−1
λ is chosen visually or by validation
Least-squares
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Choosing smoothing via validation
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split data into train and test sets, e.g., test set is last period (P
entries)
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train model on train set, and test on the test set
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choose λ to (approximately) minimize error on the test set
Least-squares
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Validation of smoothing for Melbourne temperature
trained on first 8 years; tested on last two years
3
2.95
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Least-squares
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Periodic component of temperature with smoothing
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zoomed on test set, using λ = 30
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Least-squares
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Outline
Introduction
Linear operations
Least-squares
Prediction
Prediction
30
Prediction
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goal: predict or guess xt+K given x1 , . . . , xt
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K = 1 is one-step-ahead prediction
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prediction is often denoted x̂t+K , or more explicitly x̂(t+K|t)
(estimate of xt+K at time t)
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x̂t+K − xt+K is prediction error
applications: predict
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–
–
–
–
–
Prediction
asset price
product demand
electricity usage
economic activity
position of vehicle
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Some simple predictors
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constant: x̂t+K = a
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current value: x̂t+K = xt
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linear (affine) extrapolation from last two values:
x̂t+K = xt + K(xt − xt−1 )
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average to date: x̂t+K = avg(x1:t )
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(M + 1)-period rolling average: x̂t+K = avg(x(t−M ):t )
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straight-line fit to date (i.e., based on x1:t )
Prediction
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Auto-regressive predictor
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auto-regressive predictor:
x̂t+K = (xt , xt−1 , . . . , x(t−M ) )T β + v
– M is memory length
– (M + 1)-vector β gives predictor weights; v is offset
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prediction x̂t+K is affine function of past window xt−M :t
(which of the simple predictors above have this form?)
Prediction
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Least-squares fitting of auto-regressive models
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choose coefficients β, offset v via least-squares (regression)
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regressors are (M + 1)-vectors
x1:(M +1) , . . . , x(N −M ):N
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outcomes are numbers
x̂M +K+1 , . . . , x̂N +K
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can add regularization on β
Prediction
34
Evaluating predictions with validation
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for simple methods: evaluate RMS prediction error
for more sophisticated methods:
– split data into a training set and a test set (usually sequential)
– train prediction on training data
– test on test data
Prediction
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Example
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predict Texas energy usage one step ahead (K = 1)
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train on first 10 months, test on last 2
Prediction
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Coefficients
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using M = 100
0 is the coefficient for today
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Prediction
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Auto-regressive prediction results
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x 10
Prediction
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Auto-regressive prediction results
showing the residual
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x 10
Prediction
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Auto-regressive prediction results
predictor
average (constant)
current value
auto-regressive (M = 10)
auto-regressive (M = 100)
Prediction
RMS error
1.20
0.119
0.073
0.051
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Autoregressive model on residuals
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fit a model to the time series, e.g., linear or periodic
subtract this model from the original signal to compute residuals
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apply auto-regressive model to predict residuals
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can add predicted residuals back to model to obtain predictions
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Prediction
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Example
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Melbourne temperature data residuals
zoomed on 100 days in test set
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Prediction
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Auto-regressive prediction of residuals
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Prediction
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Prediction results for Melbourne temperature
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tested on last two years
predictor
average
current value
periodic (no smoothing)
periodic (smoothing, λ = 30)
auto-regressive (M = 3)
auto-regressive (M = 20)
auto-regressive on residual (M = 20)
Prediction
RMS error
4.12
2.57
2.71
2.62
2.44
2.27
2.22
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