Automated Time Series Processing and Forecasting

• nixtla

  8 1,388 9.5 Jupyter Notebook

  Python SDK for TimeGPT, a foundational time series model

  

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 Forecast obtaining better results.

Besides the better performance and scalability, the pipeline we created considering all the stages of time series forecasting: preprocessing (e.g. missing value imputation), creation of static and dynamic features, forecast generation, and finally evaluation using data sets of important competencies. (https://github.com/Nixtla/tsfeatures)

On the deployment side, the entire pipeline can be quickly deployed in the user's cloud using terraform. This allows for less development time. (https://github.com/Nixtla/nixtla)

• nixtlats

  2 11 0.0 Jupyter Notebook

  Deep Learning for Time Series Forecasting.

  

Users can use their own models. Just create a fork of the repo and make the appropriate modifications to include any model the user wants to deploy. On our side, we are working to include Deep Learning models with the nixtlats library (https://github.com/nixtla/nixtlats/) that we also developed.

About benchmarking using statistical models, we highly recommend using statsforecast (https://github.com/Nixtla/statsforecast) that we created. It is designed to be highly efficient in fitting statistical models on millions of time series. More complex models can be built on the results to get a positive Forecast Value Added.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• darts

  47 7,232 9.1 Python

  A python library for user-friendly forecasting and anomaly detection on time series.

  

Great, another open source tool purporting to solve time series analysis in an "automated way" (lol @ attempting automated statistics) that my manager will link me tomorrow and ask me to review.

Why should I use this over Darts[1] or just Statsmodels[2], if I need more lower level access and diagnostics? Both of these are far more established.

I dislike that Facebook Prophet was chosen as a benchmark; it's not a difficult benchmark to beat for the majority of time series use cases. It signifies to me that this project might targeting cargo cult data science. Prophet is not particularly good at non-daily timeseries and non-seasonal timeseries. The paper itself admits this[3]. Moreover, it's just a generalized additive model that incorporates holidays.

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1 https://github.com/unit8co/darts

2 https://www.statsmodels.org/stable/index.html

3 https://peerj.com/preprints/3190/

• mlforecast

  11 707 8.8 Python

  Scalable machine 🤖 learning for time series forecasting.

  

We missed that, sorry. At the moment, for forecasting the pipeline uses the mlforecast library (https://github.com/nixtla/mlforecast) that builds upong Sckilearn .xgboos and lightbmg .

• tsfeatures

  5 317 5.3 Python

  Calculates various features from time series data. Python implementation of the R package tsfeatures.

  

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 Forecast obtaining better results.

Besides the better performance and scalability, the pipeline we created considering all the stages of time series forecasting: preprocessing (e.g. missing value imputation), creation of static and dynamic features, forecast generation, and finally evaluation using data sets of important competencies. (https://github.com/Nixtla/tsfeatures)

On the deployment side, the entire pipeline can be quickly deployed in the user's cloud using terraform. This allows for less development time. (https://github.com/Nixtla/nixtla)

• statsforecast

  58 3,519 8.9 Python

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  

Users can use their own models. Just create a fork of the repo and make the appropriate modifications to include any model the user wants to deploy. On our side, we are working to include Deep Learning models with the nixtlats library (https://github.com/nixtla/nixtlats/) that we also developed.

About benchmarking using statistical models, we highly recommend using statsforecast (https://github.com/Nixtla/statsforecast) that we created. It is designed to be highly efficient in fitting statistical models on millions of time series. More complex models can be built on the results to get a positive Forecast Value Added.

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