Comments (4)
Hi, do you still have a issue ? If not I will close this thread.
Please be advised that a huge update will soon be published (near Febuary) modifying a lot of parts of the current code. Try not using this version into a final product or anything too "important".
from convst.
Hi Sorry for the delay, I was not sure if this was the right place to ask, but here goes: really excited about this library, want to use it in our production code when it becomes available, but not sure about how to format the input data. Also is this library helpful in shapelet discovery ? ie. creating a database of unique shapelets (occurring under the influence of different external factors).
Currently we have two different sets of the data: the raw time series (single sensor data) and features AND the second set, which is a pre-discovered set of shapelets containing location and distance information. Some inputs on the type of Input DataFrame you are expecting will be helpful. Thanks !!
from convst.
Concerning shapelet discovery, it would depend on the usage you are planning. Shapelets parameter (length and dilation) are drawn randomly as a function of the input length, but their values are not totally random as we try to estimate the location of the discriminant information between classes.
So if you want to use it to make sense of the model you learned I don't see any issue, for other uses, you might want to first establish if this "randomness" could have a negative impact. I would need a bit more detail on your task to provide guidance on this one.
The case of shapelet discovery could nevertheless be interesting to include as a kind of human guided search with a GUI in future works on this project.
About the input data format, internally the algorithm work on a 3D numpy array of shape (n_samples, n_features, n_timestamps)
. So for your raw sensor data, we would take (n_samples, 1, n_timestamps)
. Depending on the dataframe format you are using, you could also pass a dataframe as input, as it should normally be converted to this format by the check_array_3D(X, is_univariate=True)
function.
How did you format your dataframe ? Do you have any error currently by using the library ? If so, give some detail here so i can work on a fix.
Note that in the future version, the changes made to the algorithm will allow us to support multivariate and uneven length time series, along with a supervised or non supervised shapelet extraction. But this code currently only support univariate and even length time series in a supervised context.
from convst.
Hi, thank you so much, this was extremely insightful and helpful, I am going to follow up with few experiments and come back to you with the results... cheers !!
from convst.
Related Issues (20)
- Improve docs HOT 1
- Normalization for Ridge pipelines HOT 1
- [BUG] v0.1.5.2 : n_jobs computation with n_jobs=-1
- [BUG] v0.1.5.2 numba function get_subsequence return nan values
- [BUG] v0.2.0 Loss of time performance for RDST Ensemble compared to experimental build HOT 2
- [BUG] v0.2.0 Major performance loss for some dataset HOT 1
- [BUG] Alpha similarity with multiple input lengths.
- RDST parallelism KeyError HOT 17
- Make public code used to draw diagrams
- Update results folder with latest results
- Prime dilation slower on some cases HOT 3
- unexpected keyword argument 'n_jobs_rdst' HOT 1
- Dependencies update for Python 3.11 HOT 1
- More options for shapelet sampling
- Alpha similarity mask for multivariate time series
- [BUG] Multivariate channel initialisation on v0.2.6 HOT 1
- extract distances and transformed shapelets from rdst buildt model HOT 3
- SystemError: _PyEval_EvalFrameDefault returned a result with an error set HOT 2
- [BUG] BadZipFile and ValueError on Wafer Dataset HOT 2
- Shapelet and TS extraction HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from convst.