I am pretty new on anomaly detection on time sequence so my question can be obvious for some of you. Today, I am using lstm and clustering techniques to detect anomalies on time sequences but those method can not identify anomalies that get worse slowly over the time (i think it called trending), i.e temprature of machine increase slowly over the month (lstm will learn this trend and predict the increase without any special error). There is such a method to detect this kind of faluts?
Fault Detection on time sequence of variable changing (trending) over the time
137 views Asked by user1940350 At
1
There are 1 answers
Related Questions in MACHINE-LEARNING
- Elasticsearch schema for multiple versions of the same text
- Elasticsearch nested filter query
- Elasticsearch data model
- search with filter by token count
- Usage of - operator in elasticsearch
- Running multiprocessing on two different functions in Python 2.7
- How to get an Elasticsearch aggregation with multiple fields
- How to implement custom sort in elasticsearch?
- Custom Analyzer not working Elasticsearch
- How to implement full text search using Elasticsearch in Rails?
Related Questions in CLUSTER-ANALYSIS
- Elasticsearch schema for multiple versions of the same text
- Elasticsearch nested filter query
- Elasticsearch data model
- search with filter by token count
- Usage of - operator in elasticsearch
- Running multiprocessing on two different functions in Python 2.7
- How to get an Elasticsearch aggregation with multiple fields
- How to implement custom sort in elasticsearch?
- Custom Analyzer not working Elasticsearch
- How to implement full text search using Elasticsearch in Rails?
Related Questions in TREND
- Elasticsearch schema for multiple versions of the same text
- Elasticsearch nested filter query
- Elasticsearch data model
- search with filter by token count
- Usage of - operator in elasticsearch
- Running multiprocessing on two different functions in Python 2.7
- How to get an Elasticsearch aggregation with multiple fields
- How to implement custom sort in elasticsearch?
- Custom Analyzer not working Elasticsearch
- How to implement full text search using Elasticsearch in Rails?
Related Questions in ANOMALY-DETECTION
- Elasticsearch schema for multiple versions of the same text
- Elasticsearch nested filter query
- Elasticsearch data model
- search with filter by token count
- Usage of - operator in elasticsearch
- Running multiprocessing on two different functions in Python 2.7
- How to get an Elasticsearch aggregation with multiple fields
- How to implement custom sort in elasticsearch?
- Custom Analyzer not working Elasticsearch
- How to implement full text search using Elasticsearch in Rails?
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Popular Tags
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
With time series that is usually what you want: learning gradual change, detecting abrupt change. Otherwise, time plays little role.
You can try e.g. the SigniTrend model with a very slow learning rate (a long half-life time or whatever they called it. Ignore all the tokens, hashing and scalability in that paper, only get the EWMA+EWMVar part which I really like and use it on your time series).
If you set the learning rate really low, the threshold should move slow enough so that your "gradual" change may still be able to trigger them.
Or you ignore time completely. Split your data into a training set (that must not contain anomalies), learn mean and variance on that to find thresholds. Then classify any point outside these thresholds as abnormal (I.e. temperature > mean + 3 * standarddeviation). As this super naive approach does not learn, it will not follow a drift either. But then time does not play any further role.