fedegr

8 ms·

1. 1.▲
   by fedegr 4y ago

   I completely agree with your perspective. It is a reality that deep learning models might offer certain advantages over classical statistical models. We are building benchmarks and comparisons to clarify when the more complex models are bet
2. 2.▲
   by fedegr 4y ago

   Co-author here: all in due time. Next iteration we will include LigthGBM, XGBoost, and newer DL models like TFT and NHiTS.
3. 3.▲
   by fedegr 4y ago

   SeasonalNaive is 20% more accurate than Prophet or NeuralProphet and 366 times faster for next-day electricity demand forecasting.
4. 4.▲
   Simple AutoML for time series with Ray Core(docs.ray.io)

   3 points by fedegr 4y ago|0 comments
5. 5.▲
   by fedegr 4y ago

   NeuralProphet is the successor-extension of Prophet, and it aims to provide Prophet with neural networks and autoregressive terms. The paper can be found here ( https://arxiv.org/abs/2111.15397?fbclid=IwAR2vCkHYiy5yuPPjW
6. 6.▲
   by fedegr 4y ago

   I think the problem arises from the datasets used to evaluate the performance of the models. In the case of Prophet's paper, only one time series is used (The number of events created on Facebook). We can conclude from the results comp
7. 7.▲
   Is linear regression better than prophet? Zillow benchmark(github.com)

   2 points by fedegr 5y ago|1 comments
8. 8.▲
   by fedegr 5y ago

   Yes. We found evidence that linear regression with feature engineering is better than prophet on Zillow data.
9. 9.▲
   Prophet vs. Linear Regression on Real Estate: The Zillow Case(github.com)

   1 points by fedegr 5y ago|0 comments
10. 10.▲
    by fedegr 5y ago

    The pipeline we have developed improves the state of the art in the markets you mention in the following aspects: 1. It is a fully automated end-to-end pipeline for forecast generation. The pipeline considers preprocessing such as missing v
11. 11.▲
    by fedegr 5y ago

    We missed that, sorry. At the moment, for forecasting the pipeline uses the mlforecast library ( https://github.com/nixtla/mlforecast ) that builds upon sklearn, xgboost and lightgbm. In addition, we are about to inclu
12. 12.▲
    by fedegr 5y ago

    Thanks for your comments. We agree that in most cases prophet is not a good benchmark; however, we wanted to use it because it is one of the most used libraries in forecasting. For that reason, we also tested the solution against AWS Foreca
13. 13.▲
    Automated Time Series Processing and Forecasting(github.com)

    38 points by fedegr 5y ago|8 comments