Tagged Questions

3
votes
0answers
38 views

Link Anomaly Detection in Temporal Network

I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
2
votes
1answer
39 views

Filtering outliers from geo-spatial-temporal log

I have downloaded my Latitude location history from Google for the time of about three years and now I'd like to, for starters, visualize where I've been. It turns out that the history contains some ...
1
vote
0answers
42 views

Spotting anomalies in time series [duplicate]

Possible Duplicate: Simple algorithm for online outlier detection of a generic time series I've got data on quantities sold and average price, by date, for a number of commodities and a ...
3
votes
0answers
36 views

Detecting outstanding events [duplicate]

Possible Duplicate: Simple algorithm for online outlier detection of a generic time series Observing the time series data I noticed there are some outstanding peaks (the picture below). I ...
3
votes
1answer
268 views

STL on time series with missing values for anomaly detection

I am trying to detect anomalous values in a time series of climatic data with some missing observations. Searching the web I found many available approaches. Of those, stl decomposition seems ...
0
votes
2answers
198 views

Filtering techniques and noise

Suppose we have some house price data for 30 years (1970-1999). This is yearly data (30 data points). Suppose some major event $X$ happened on 1980. I want to see whether this event affected prices ...
5
votes
3answers
572 views

How to fit a model for a time series that contains outliers

I have fitted ARIMA(5,1,2) model using auto.arima() function in R and by looking order we can say this is not a best model to forecast. If outliers exist in the ...
1
vote
1answer
156 views

Treatment of outlier in annual time series data

I have the annual time series data of growth variable $X$ for 50 years. Most of the values for the variable $X$ are less than 10%. The exception are two values that are around 30%. How do I treat ...
1
vote
3answers
312 views

Univariate clustering of time series

I just want to know if its possible to cluster an univariate time series, in order , say, to detect anomalies? and do you have any online version for denstream code, in Matlab? here is the time ...
1
vote
1answer
440 views

C++ library to play with statistics (detecting outliers in time series)

I am looking for a C++ library for statistics to play with outliers detection in time series (amongst other). What I need: Robust estimators, correlations, hypothesis tests, etc; No dependencies ...
4
votes
2answers
199 views

How to judge if a datapoint deviates substantially from the norm

This is statistics 101, but I'm not a statistician and so can't seem to find the right technical jargon to google. My company collects data at discreet points through time. Today's datapoint is ...
1
vote
0answers
40 views

Modeling a TAR model that can handle the problem of outliers in nonlinear data

Can somebody please share idea on how I can model a TAR model that can handle outliers in nonlinear data? I need to compare such model with the general form of TAR model. Which computer software can ...
3
votes
5answers
779 views

Use of robust spread measures such median average deviation and median filters for time series

I have a time series where I need to detect gross anomalies due to coding errors, not small shifts in the structure of the series. I am interested in the most recent data points, not historical data ...
5
votes
3answers
837 views

Outliers spotting in time series analysis, should I pre-process data or not?

My question builds on a previous post on outlier detection in generic time series, and specifically on the answer provided by the always great Rob H. I work for a small-sized manufacturing company ...
2
votes
1answer
108 views

Find outlier in time domain dataset

We're analyzing a bunch of time domain signals, I want to be able to identify an outlying one. In our results all the signals will either all be reasonably similar, or in some cases, one should be ...
1
vote
1answer
320 views

Bonferroni for outlier detection?

I am reading a book on time series analysis and I am having problems understanding the section about outlier detection. The authors say that when you want to know whether at a certain time $T$ there ...
2
votes
1answer
190 views

Outlier detection in short time series with two seasonalities

I have short daily time series (less than 4 years) representing sales and exhibiting two seasonalities (weekly and yearly) and I am seeking to identify outliers (not only data reporting errors but ...
4
votes
2answers
502 views

Outlier detection for generic time series

In this case, "generic" being the entire gauntlet of macroeconomic time-series that private and government statistical offices put out. Some background - I recently started working at a data provider ...
6
votes
5answers
780 views

Automatic threshold determination for anomaly detection

I am working with a time series of anomaly scores (the background is anomaly detection in computer networks). Every minute, I get an anomaly score $x_t \in [0, 5]$ which tells me how "unexpected" or ...
15
votes
5answers
594 views

Can data cleaning worsen the results of statistical analysis?

An increase in the number of cases and deaths occurs during epidemics (sudden increase in numbers) due to a virus circulation (like West Nile Virus in USA in 2002) or decreasing resistance of people ...
10
votes
3answers
3k views

Robust outlier detection in financial timeseries

I'm looking for some robust techniques to remove outliers and errors (whatever the cause) from financial time-series data (i.e. tickdata). Tick-by-tick financial time-series data is very messy. It ...
23
votes
11answers
5k views

Simple algorithm for online outlier detection of a generic time series

I am working with a large amount of time series. These time series are basically network measurements coming every 10 minutes, and some of them are periodic (i.e. the bandwidth), while some other ...
11
votes
3answers
1k views

Application of wavelets to time-series-based anomaly detection algorithms

I've been beginning to work my way through Statistical Data Mining Tutorials by Andrew Moore (highly recommended for anyone else first venturing into this field). I started by reading this extremely ...