add more references in README for related scientific papers

README.md

@@ -141,3 +141,10 @@ the following reference to the associated
  year={2019}
 }
 ```
+
+## Further Reading 
+
+* [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluon-ts/tree/master/REFERENCES.md): a bibliography.
+* [Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/)
+* [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/)
+* [Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/)

REFERENCES.md

@@ -0,0 +1,189 @@
+# Scientific Articles
+We encourage you to also check out work by the group behind 
+GluonTS. They are grouped according to topic and ordered 
+chronographically.
+
+## Methods
+A number of the below methods are available in GluonTS.
+
+[A multivariate forecasting model](https://arxiv.org/abs/1910.03002)
+```
+@inproceedings{salinas2019high,
+ Author = {Salinas, David and Bohlke-Schneider, Michael and Callot, Laurent and Gasthaus, Jan},
+ Booktitle = {Advances in Neural Information Processing Systems},
+ Title = {High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes},
+ Year = {2019}
+}
+```
+
+[Deep Factor models, a global-local forecasting method.](http://proceedings.mlr.press/v97/wang19k.html)
+```
+@inproceedings{wang2019deep,
+ Author = {Wang, Yuyang and Smola, Alex and Maddix, Danielle and Gasthaus, Jan and Foster, Dean and Januschowski, Tim},
+ Booktitle = {International Conference on Machine Learning},
+ Pages = {6607--6617},
+ Title = {Deep factors for forecasting},
+ Year = {2019}
+}
+```
+[DeepAR, an RNN-based probabilistic forecasting model](https://arxiv.org/abs/1704.04110)
+```
+@article{flunkert2019deepar,
+ Author = {Salinas, David and Flunkert, Valentin and Gasthaus, Jan and Tim Januschowski},
+ Journal = {International Journal of Forecasting},
+ Title = {DeepAR: Probabilistic forecasting with autoregressive recurrent networks},
+ Year = {2019}
+}
+```
+[A flexible way to model probabilistic forecasts via spline quantile forecasts.](http://proceedings.mlr.press/v89/gasthaus19a.html)
+```
+@inproceedings{gasthaus2019probabilistic,
+ Author = {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim},
+ Booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics},
+ Pages = {1901--1910},
+ Title = {Probabilistic Forecasting with Spline Quantile Function RNNs},
+ Year = {2019}
+}
+```
+[Using RNNs to parametrize State Space Models.](https://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting)
+```
+@inproceedings{rangapuram2018deep,
+ Author = {Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus, Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim},
+ Booktitle = {Advances in Neural Information Processing Systems},
+ Pages = {7785--7794},
+ Title = {Deep state space models for time series forecasting},
+ Year = {2018}
+}
+```
+[A scalable state space model. Note that code for this model
+is currently not available in GluonTS.](https://papers.nips.cc/paper/6313-bayesian-intermittent-demand-forecasting-for-large-inventories)
+```
+@inproceedings{seeger2016bayesian,
+ Author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin},
+ Booktitle = {Advances in Neural Information Processing Systems},
+ Pages = {4646--4654},
+ Title = {Bayesian intermittent demand forecasting for large inventories},
+ Year = {2016}
+}
+```
+
+
+
+## Tutorials
+Tutorials are available in bibtex and with accompanying material,
+ in particular slides, linked from below.
+
+### KDD 2019
+[paper](https://dl.acm.org/citation.cfm?id=3332289) 
+[slides](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/)
+```
+@inproceedings{faloutsos19forecasting,
+ author = {Faloutsos, Christos and
+ Flunkert, Valentin and
+ Gasthaus, Jan and
+ Januschowski, Tim and
+ Wang, Yuyang},
+ title = {Forecasting Big Time Series: Theory and Practice},
+ booktitle = {Proceedings of the 25th {ACM} {SIGKDD} International Conference on
+ Knowledge Discovery {\&} Data Mining, {KDD} 2019, Anchorage, AK,
+ USA, August 4-8, 2019.},
+ year = {2019}
+ }
+```
+### SIGMOD 2019
+[paper](https://dl.acm.org/citation.cfm?id=3314033&dl=ACM&coll=DL)
+[supporting material](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/)
+```
+@inproceedings{faloutsos2019classical,
+ author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang},
+ title = {Classical and Contemporary Approaches to Big Time Series Forecasting},
+ booktitle = {Proceedings of the 2019 International Conference on Management of Data},
+ series = {SIGMOD '19},
+ publisher = {ACM},
+ address = {New York, NY, USA},
+ year = {2019}
+} 
+```
+### VLDB 2018
+[paper](http://www.vldb.org/pvldb/vol11/p2102-faloutsos.pdf)
+[supporting material](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/)
+```
+@article{faloutsos2018forecasting,
+ Author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang},
+ Journal = {Proceedings of the VLDB Endowment},
+ Number = {12},
+ Pages = {2102--2105},
+ Title = {Forecasting big time series: old and new},
+ Volume = {11},
+ Year = {2018}
+}
+```
+
+## General audience
+An overview of forecasting libraries in Python.
+[paper to appear](https://foresight.forecasters.org/wp-content/uploads/Foresight_Issue55_cumTOC.pdf)
+```
+@article{januschowski19open,
+ title={Open-Source Forecasting Tools in Python},
+ author={Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang},
+ journal={Foresight: The International Journal of Applied Forecasting},
+ year={2019}
+}
+```
+[A commentary on the M4 competition and its classification of the participating methods 
+into 'statistical' and 'ML' methods. The article proposes alternative criteria.](https://www.sciencedirect.com/science/article/pii/S0169207019301529)
+```
+@article{januschowski19criteria,
+title = {Criteria for classifying forecasting methods},
+author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Salinas, David and Flunkert, Valentin and Bohlke-Schneider, Michael and Callot, Laurent},
+journal = {International Journal of Forecasting},
+year = {2019}
+}
+```
+[The business forecasting problem landscape can be divided into 
+strategic, tactical and operational forecasting problems.](https://foresight.forecasters.org/product/foresight-issue-53/)
+```
+@article{januschowski18a,
+ title={A Classification of Business Forecasting Problems},
+ author={Januschowski, Tim and Kolassa, Stephan},
+ journal={Foresight: The International Journal of Applied Forecasting},
+ year={2019},
+ volume={52}, 
+ pages={36-43}
+}
+```
+A two-part article introducing deep learning for forecasting.
+[part 2](https://foresight.forecasters.org/product/foresight-issue-52/)
+[part 1](https://foresight.forecasters.org/product/foresight-issue-51/)
+```
+@article{januschowski18deep2,
+title = {Deep Learning for Forecasting: Current Trends and Challenges},
+journal = {Foresight: The International Journal of Applied Forecasting},
+year = {2018},
+author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent},
+volume = {51}, 
+pages = {42-47}
+}
+```
+```
+@article{januschowski18deep,
+ title = {Deep Learning for Forecasting},
+ author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama and Callot, Laurent},
+ journal = {Foresight},
+ year = {2018}
+}
+```
+
+## System Aspects
+[A large-scale retail forecasting system.](http://www.vldb.org/pvldb/vol10/p1694-schelter.pdf)
+```
+@article{bose2017probabilistic,
+ Author = {B{\"o}se, Joos-Hendrik and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Lange, Dustin and Salinas, David and Schelter, Sebastian and Seeger, Matthias and Wang, Yuyang},
+ Journal = {Proceedings of the VLDB Endowment},
+ Number = {12},
+ Pages = {1694--1705},
+ Title = {Probabilistic demand forecasting at scale},
+ Volume = {10},
+ Year = {2017}
+}
+```