msrdl / deep4cast Goto Github PK
View Code? Open in Web Editor NEWProbabilistic Multivariate Time Series Forecast using Deep Learning
License: BSD 3-Clause "New" or "Revised" License
Probabilistic Multivariate Time Series Forecast using Deep Learning
License: BSD 3-Clause "New" or "Revised" License
For quick testing and productionization
Resource: https://arxiv.org/pdf/1703.04691.pdf
Hyperoptimizer issues error tracing back to CNTK. Same code runs fine in tensorflow. Error can be observed in tutorials/hyperoptimization.ipynb
Hi,
In the tutorial on Github traffic data, how the input_channel
parameter's value was set to 21? An explanation of that would be helpful.
Users should be able to input either a WaveNet
model, a string 'wavenet'
, 'gru'
, 'lstm'
, etc. No matter how cool WaveNet is, many users would expect to be able to use LSTM
.
This allows a more diverse set of models to be built using a small and flexible codebase
We should add a stacked GRU with residual connections to have this in our model repertoire.
This can be built using straight up pytorch, should also include an example tutorial.
Maybe combine with initialization issue.
Resource for Keras code:
https://github.com/yaringal/ConcreteDropout
Winning Kaggle entry for web traffic forecasting:
https://github.com/shellshock1911/Sky-Cast-Capstone/blob/master/final_report.pdf
https://github.com/Arturus/kaggle-web-traffic
https://github.com/Arturus/kaggle-web-traffic/blob/master/how_it_works.md
github_total_push_events_2011-2018.csv file that is to be read in is wrongly named and should be github_dau_2011-2018.csv to match with the data file in raw
Add static and time-dependent covariates to fit and predict methods in forecaster module. I suspect it is best to have keras model that exepcts covariates all conform to the same interface in terms of how they expect the input data to propagate through the network.
Create readthedocs.org documentation that documents how the frameworks works,
GefCom has a bivariate time series dataset. It's nicer to show bivariate case.
Make the number of levels
parameter match the severity levels in business rather than a sensitive parameter with no physical meaning in business.
Once we have that, then users wouldn't even need to input the number of anomalies
parameter and still obtain a good anomaly detection model.
Ideally we have a very clear and clean set of script to run models on benchmark datasets.
We want input and output dimensions to be user specified when initialization the model and forecaster class. This causes problems, because we have currently delayed initializations. For example, no real custom models or loss functions are currently accepted by the forecaster.
This makes it easier for the user to understand what the user can provide.
This function takes the following data specs <input dimension, output dimension, history length, forecast horizon> as input and outputs the network topology.
Do this for CNN first. This would eliminate the need to parameter sweeping for CNNs, which include lots of nested parameters.
Horizon
= 50, Lag
= 500 wrt original timestampDownsample rate
: 1, 2, 3, 10, 25, 50Sub-lag
: Lag
/Downsample rate
Extensions:
sub-lag
(input-size)and integrate it into this library if it works well.
Also compare this with the Tensor-Train RNNs paper.
The dimensionality of the data need to be specified as types and comments in the forecaster.
To allow support for large datasets (10gb+).
We need to have a custom loss function similar to the ones from this paper:
https://arxiv.org/abs/1703.04977
Could be distributional losses, or CDF-based losses, or combinations.
Let's build this after the AI class
Build multi-step loss function into model. Capture aleatoric uncertainty in loss function.
distributeddataparallel claims better performance than dataparallel
Take the notebook template and refactor into Python functions and small notebook.
We will use this as the very first baseline.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.