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Today we are glad to announce these exciting features! 🎉
* Improved StatsForecast class
* Selection of best-performing models
* New documentation
* Plotting functionalities
Stay tuned for an exciting polemic comparison tomorrow!
#reinventforecasting
* Improved StatsForecast class
* Selection of best-performing models
* New documentation
* Plotting functionalities
Stay tuned for an exciting polemic comparison tomorrow!
#reinventforecasting
StatsForecast class:
With the new StatsForecast class, you can fit many models for many time series in less than 15 lines of code
You can include @dask_dev, @raydistributed, and #spark by changing just the backend parameter
With the new StatsForecast class, you can fit many models for many time series in less than 15 lines of code
You can include @dask_dev, @raydistributed, and #spark by changing just the backend parameter
Models: StatsForecast 27 models.
They can be included and imported in a few lines of code.
They can be included and imported in a few lines of code.
In today's exciting release😎
Use @nixtlainc's StatsForecast to beat a WIDE variety of #DeepLearning models with:
- interpretable methods 📈
- in under 10 min ⏲️
- $0.5c in AWS 🔥
- and a few lines of #python code 🤯
#reinventforecasting
🧵 1/5
Use @nixtlainc's StatsForecast to beat a WIDE variety of #DeepLearning models with:
- interpretable methods 📈
- in under 10 min ⏲️
- $0.5c in AWS 🔥
- and a few lines of #python code 🤯
#reinventforecasting
🧵 1/5
🧵2/5
Building upon the great work of @spyrosmakrid et al, we fitted an ensemble of simple statistical models:
AutoARIMA, Exponential Smoothing (@robjhyndman etal),
Complex Exponential Smoothing (@iSvetunkov etal)
and the DOT method (@fotpetr etal)
The results were great!🔥
Building upon the great work of @spyrosmakrid et al, we fitted an ensemble of simple statistical models:
AutoARIMA, Exponential Smoothing (@robjhyndman etal),
Complex Exponential Smoothing (@iSvetunkov etal)
and the DOT method (@fotpetr etal)
The results were great!🔥
🧵3/5
The simple statistical ensemble:
- outperforms most #DeepLearning models 🔥
- is 25,000 faster ⚡️
- slightly less accurate than an #DeepLearning ensemble 🤔
The simple statistical ensemble:
- outperforms most #DeepLearning models 🔥
- is 25,000 faster ⚡️
- slightly less accurate than an #DeepLearning ensemble 🤔
Today at @nixtlainc: fast and interpretable forecasting for multiple seasonalities. ⏲️🔎
This is exciting news for data scientists in the Python ecosystem. 🧵
#reinventforecasting
This is exciting news for data scientists in the Python ecosystem. 🧵
#reinventforecasting
Time series are the operational DNA of the world. 🧬
Most business data (sales, demand, electricity load, sensors) is stored in tabular format with time indexes.
Most forecasting models in Python are only suited for single seasonal data. ⚠️
#reinventforecasting
Most business data (sales, demand, electricity load, sensors) is stored in tabular format with time indexes.
Most forecasting models in Python are only suited for single seasonal data. ⚠️
#reinventforecasting
However, many time series have multiple seasonalities.
Multiple seasonality is traditionally present in data sampled at a low frequency. 💡
For example, hourly electricity data exhibits daily and weekly seasonality. 🎄🕎❄️
#reinventforecasting
Multiple seasonality is traditionally present in data sampled at a low frequency. 💡
For example, hourly electricity data exhibits daily and weekly seasonality. 🎄🕎❄️
#reinventforecasting
@nixtlainc started last year as a side project
Today we reached 1 million downloads🎉
Our goal is to shake the time series industry and make state-of-the-art algorithms available for everyone 🔥
This is how we got there
🧵1/11
#reinventforecasting
Today we reached 1 million downloads🎉
Our goal is to shake the time series industry and make state-of-the-art algorithms available for everyone 🔥
This is how we got there
🧵1/11
#reinventforecasting
@nixtlainc's ecosystem consists (now) of 5 #python libraries
Focus: speed, scalability, and accuracy 🚀
Some features:
* @scikit_learn syntax
* Native support for #spark, @raydistributed, and @dask_dev
* Models! Eg #arima, #LightGBM, #NeuralNetworks, #transformers 🤖
2/11
Focus: speed, scalability, and accuracy 🚀
Some features:
* @scikit_learn syntax
* Native support for #spark, @raydistributed, and @dask_dev
* Models! Eg #arima, #LightGBM, #NeuralNetworks, #transformers 🤖
2/11
We are really proud of the open-source adoption
Repos from Amazon, Mozilla, and DataBricks use us as dependencies 🏄♂️
We have contributions from people working at H20, Microsoft, Google, Facebook, SalesForce, Oracle, Shopify, AT&T, Blueyonder, Stanford, MIT and UCL 🙏
3/11
Repos from Amazon, Mozilla, and DataBricks use us as dependencies 🏄♂️
We have contributions from people working at H20, Microsoft, Google, Facebook, SalesForce, Oracle, Shopify, AT&T, Blueyonder, Stanford, MIT and UCL 🙏
3/11
