Foundations  2. Periodicity Detection, Timeseries Correlation, Burst Detection. Temporal Information Retrieval


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1 Foundations  2 Periodicity Detection, Timeseries Correlation, Burst Detection Temporal Information Retrieval
2 Time Series An ordered sequence of values (data points) of variables at equally spaced time intervals
3 Periodicity Detection How does one identify periodic values
4 Periodicity Detection Timeseries is in the time domain Method1 (DFT): Identify the underlying periodic patterns by transforming into the frequency domain Method 2 (Autocorrelation) Correlate the signal with itself Find dominant frequencies
5 Fourier Transform A signal has an amplitude (strength), frequency (periodicity) and phase (offset) phase frequency amplitude Fourier Transform converts a signal from the time domain to the frequency domain
6 Discrete Fourier Transform (DFT) A Fourier analysis is a method for expressing a function as a sum of periodic components, and for recovering the function from those components. When both the function and its Fourier transform are replaced with discretized counterparts, it is called the discrete Fourier transform (DFT). fourier transform f 4 f 3 f 2 f 1 f 0 inv. fourier transform Advantages of DFT apart from periodicity detection? denoising, compression
7 Discrete Fourier Transform (DFT) fourier coefficients fourier transform F[n] periodogram f (Hz) sinusoid X(f k/n )= p 1 NX 1 N n=0 x(n) e j2 kn N The fourier coefficients encode both the amplitude and phase
8 Power Spectral Density (PSD) Estimation fourier transform F[n] f (Hz) To find out the dominant frequency we need to find the power at each frequency Periodogram encodes the strength at a given frequency P(f k/n )= X(f k/n ) 2 k =0, 1... N 1 2
9 PSD estimation using Periodogram time series data or signal P2= P1= 7 periodogram P(f k/n )= X(f k/n ) 2 k =0, 1... N To find the dominant frequencies choose the topk dominant frequencies
10 Disadvantages of the Periodogram time series data or signal P2= P1= 7 periodogram P(f k/n )= X(f k/n ) 2 k =0, 1... N Good only for short and medium periodicities Spectral leakage  frequencies not integral multiples of the DFT bin spread over other bins false alarms
11 Autocorrelation Correlate the time series with itself Peaks get amplified ACF ( ) = P N i=1 Y i Y i+ N Finegrained periodicity detector
12 Autocorrelation time series data or signal 0 x day Autocorrelation ACF ( ) = P N i=1 Y i Y i+ N To determine dominant period significance threshold needs to be specified Multiples of the same period are also peaks needs post processing
13 AutoPeriod Autocorrelation : Good for large periods but difficult to automatically determine periods Periodogram : Easy to threshold but not accurate for short periods Idea: Get candidate periods from Periodogram and validate false alarms using Autocorrelation hill Sequence Periodogram Candidate Autocorrelation Refine Period Period Periods valley False Alarm Dismiss
14 AutoPeriod P1= P2= P3= Periodogram Circular Autocorrelation Valley: False Alarms Hill: Candidate Period 357: correct period
15 Matching Time Series Similar time series suggest similar things
16 Matching Time Series Correlating time series used for clustering, classification, anomaly detection, speech recognition etc.
17 Matching Time Series What measure would you use to match two time series? d = X t y t x t Euclidean Distance Why is Euclidean matching not good enough?
18 Dynamic Time Warping Time series might be shifted Time series might be compressed at some point in time Random noise at some points Dynamic time warping measures the distance between two sequences under certain restrictions. Not a metric. Triangle inequality doesn't hold
19 Detour  Edit Distance Edit distance measures how many steps it takes to convert a string to another based on restrictions Restrictions define cost function insertion, deletion, replacement f o x f o x f f a a x x insertions and deletions insertions, deletions and replacements
20 Edit Distance f o x f o x f f a a x x insertions and deletions insertions, deletions and replacements d ij = 8 d i 1,j >< 8 1 a j = b i >< d i 1,j + w del (b i ) 1, for 1 apple i apple m, 1 apple j apple n. min d i,j 1 + w ins (a j ) 1 a j 6= b i >: >: d i 1,j 1 + w sub (a j,b i ) 1 or 2
21 Dynamic Time Warping DTW aligns two sequences of feature vectors by warping the time axis iteratively until an optimal match (according to a suitable metrics) between the two sequences is found. x 1,x 2,...x n y 1,y 2,...y n d(i, j) =c(i, j)+min 8 >< d(i 1,j), d(i 1,j 1), >: d(i, j 1)
22 Burst Detection Bursts are rare but extremely beneficial in timeseries Used in number of applications Twitter: Trending topics Stock markets: Trending Stocks Text Mining: finding important time periods Elastic burst detection: Stream of data Quadratic computations not allowed
23 Burst Detection Global Average Moving Average Damped Average
24 Elastic Burst Detection Given a timeseries {xi} A set of window sizes W A monotonic, associative aggregation function A which maps a sequence of values to a number. E.g. Average, Max and Thresholds associated with each window size w, f(w) Find all pairs (t,w) such that t time a time point and w is a window size in W A[x t x t+w 1 ] f(w).
25 Burst Detection  Shifted Binary Tree Level 4 Level 3 Level 2 base level shifted level Level 1 Level 0 Whenever more than f(2 + 2i 1) events are found in a window of size 2i+1, then a detailed search must be performed to check if some subwindow of size w, 2+2i 1 w 1+2i, has f(w) events.
26 Summary Periodicity of Events Autocorrelation, Periodograms and their combinations Burst Detection Techniques and elastic detection Matching of time series Euclidean matching Dynamic Time Warping
27 References On Periodicity Detection and Structural Periodic Similarity, Michail Vlachos, Philip Yu, Vittorio Castelli Burst Detection in Hierarchical Streams. Jon Kleinberg Everything you know about Dynamic Time Warping is wrong Eamonn Keogh
28 Projects Temporal and Phrasebased Indexing  Temporal Retrieval Models  Jaspreet Temporal Query Autocompletion Avishek Crawling for Temporal Collections  Gerhard Temporal Query Suggestions  Helge
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