Linked Questions

2 votes
1 answer

Finding anomalies using moving average in a time series [duplicate]

I want to find anomalies in a time series. Is it possible to find anomalies using moving average?
  • 419
3 votes
0 answers

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 would ...
  • 1,353
1 vote
0 answers

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 number ...
  • 111
0 votes
0 answers

Getting rid of sparks in sample data [duplicate]

Possible Duplicate: Simple algorithm for online outlier detection of a generic time series Getting rid of spikes in sample data How could I get rid of sparky (aka spikey) data in a discrete ...
  • 101
3 votes
0 answers

Application to a real life problem: identifying outliers in univariate time series data [duplicate]

I have a rather simple problem that I'm having trouble deciding an answer upon. As a student studying Statistics I'm very familiar with terminology and theory but I suppose I'm stumped on my first ...
  • 31
2 votes
0 answers

Anomaly detection within periodic time series [duplicate]

I would like to detect anomalies in a time series data using a moving average approach. Basically, the system will rise an alarm when a data point is far away (3*std for example) from the current mean....
  • 283
0 votes
0 answers

Getting rid of spikes in sample data [duplicate]

Possible Duplicate: Simple algorithm for online outlier detection of a generic time series How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner? Take for ...
  • 101
61 votes
7 answers

Period detection of a generic time series

This post is the continuation of another post related to a generic method for outlier detection in time series. Basically, at this point I'm interested in a robust way to discover the periodicity/...
  • 1,951
18 votes
3 answers

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 ...
15 votes
5 answers

How to correct outliers once detected for time series data forecasting?

I'm trying to find a way of correcting outliers once I find/detect them in time series data. Some methods, like nnetar in R, give some errors for time series with big/large outliers. I already managed ...
  • 151
8 votes
3 answers

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 ...
  • 699
7 votes
3 answers

How to fit a robust step function to a time series?

I have a somewhat noisy time series that hovers around different levels. For example, the following data: I have the solid line data available, and I would like to obtain an estimate for the dashed ...
12 votes
4 answers

Outlier Detection in Time-Series: How to reduce false positives?

I'm trying to automate outlier detection in time-series and I used a modification of the solution proposed by Rob Hyndman here. Say, I measure daily visits to a website from various countries. For ...
  • 558
1 vote
3 answers

Trend Analysis: How to tell random fluctuations from actual changes in trends?

I hope somebody in here can help me: I'm looking for some pointers as to how to distinguish random fluctuation from actual changes in trends, e.g.: In a time series with measures taken at monthly (...
  • 465
4 votes
2 answers

How to identify spikes in a noisy time series?

I have time-series data of brain cell spiking. It's basically got a baseline of random noise with large spikes interspersed. I want to be able to algorithmically cluster the spike portions of the ...

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