Comments (10)
Yes, I think we can remove them. Although it would be nice to replace AutoARIMA with ARIMA.
I'll remove AutoARIMA from deps and log a warning that it's not available while keeping ARIMA. Would that it be ok for you?
edit: clarification
from darts.
@plorenzatto Thanks a lot for contributing, I think your proposed changes could be very useful! I think making torch and fbprophet optional like you suggested makes sense. But I'm a bit unsure when it comes to statsmodels, since in addition to being used in 3 models, it is also used in some functions from our statistics
module.
I believe these parts should work without any compromises in the core version:
TimeSeries
classbacktesting
module (without TorchForecastingModel support)- at least some models from the
models
module preprocessing
moduleutils
module (except the torch part)metrics
module
Pretty much everything except some of the more fancy forecasting models for now I believe. What are your thoughts?
from darts.
Hi @plorenzatto,
thank you for bringing this up - this is a very good point that's been also in our backlog for a while already.
Is it something you would like to maybe contribute yourself/are you okay with waiting until we deliver this on our side?
from darts.
I'm planning to work on this and send a PR when it's ready.
I have some questions about this:
- Which functionalities/modules must absolutely work with the default installation?
- How did you originally thought of solving this issue?
from darts.
@TheMP I'm working on #177 on this. Could you give me some input on the points that I mentioned in the previous message?
from darts.
I agree with the general idea @pennfranc.
Indeed statsmodels
is a dependency of metrics
via statistics
so better to keep it in the main requirements.
I have some doubts:
darts/tests/test_dataset.py
depends on..models.torch_forecasting_model._TimeSeriesSequentialDataset
not sure if it's meant to betorch
specific or not.backtesting.explore_models
usesAutoARIMA
andProphet
by default. Can we remove them?
from darts.
Good points.
- This is definitely specific to our
TorchForecastingModel
since it tests a subclass of ours of atorch
class (torch.utils.data.Dataset
). The naming of this test case could probably be made to better reflect this fact. - Yes, I think we can remove them. Although it would be nice to replace
AutoARIMA
withARIMA
.
from darts.
Hi - Just wondering if there is an estimate when this change might be in place. Sorry I'm not helping with the work but would like to use the u8darts library in production but previously mentioned dependencies create too large of a Docker image.
from darts.
@dboyleitrs We are evaluating our options to solve this issue. Hopefully we should be able to release this within a couple of weeks or so.
from darts.
This is implemented in develop, and will be part of the next release:
pip install u8darts
installs core onlypip install u8darts[all]
installs everythingpip install u8darts[torch]
installs core + torch modelspip install u8darts[fbprophet]
installs core + Prophetpip install u8darts[pmdarima]
installs core + AutoARIMA
from darts.
Related Issues (20)
- [Question] Help understanding warning message when trying to iteratively forecast HOT 1
- Replacing the obsolete applymap with map HOT 3
- [BUG] RegressionEnsembleModel predict error HOT 5
- Passing kwargs to the underlying fitting function HOT 1
- [BUG] Models that fit method do not support the parameter sample_weights cannot be fitted HOT 2
- [Question] should old versions of pandas be taken into account in PR HOT 2
- Global Vs Local Models and Multivariate vs Multiple Series Forecasting HOT 1
- fix: PerformanceWarning - DataFrame is highly fragmented
- Historical Backtest With Updated Covariates (Regression/ LGBM Model)
- Does Darts support mixed-frequency data? HOT 3
- Historical Backtest With Updating Covariates (Tree/ Regression Models) HOT 7
- [BUG] XGBModel parameters not forwarded to models created on grid search
- Documentation Inconsistency: `num_loader_workers` Parameter
- [Question] Ensemble model on a mix of local and global models using historical_forecasts on a univariate series HOT 5
- ARIMA with Past Covariates HOT 4
- Save model and weights into object HOT 2
- How are samples generated with RegressionModel / MLPRegressor? HOT 5
- Documentation TFT (past_ vs. future_covariates) HOT 2
- TFT Model resume training from checkpoint not matching continuous training HOT 1
- Retrieve one sample of a probabilistic series as a TimeSeries HOT 5
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 darts.