KNIME & fbProphet: Time Series Forecasting with a few clicks

Time series analysis can be very demanding and sometimes you just want to press a button instead of putting too much time and effort into setting up the analysis.

The Facebook Prophet (fbProphet) library is the solution to our problem and we want to implement it as a component in KNIME so that we only have to adjust a few settings and the whole time series analysis is done automatically.

[...]

fbProphet, also simply called Prophet, is a forecasting algorithm developed by Facebook’s data science team in 2017. The algorithm is designed to be scalable, fast, and accurate, making it suitable for a wide range of applications, from predicting sales in e-commerce to forecasting weather patterns.

Leggi tutto l'articolo in inglese: https://medium.com/low-code-for-advanced-data-science/knime-fbprophet-time-series-forecasting-with-a-few-clicks-4d527460ba8e


Approfondisci il modello previsionale fbProphet qui: https://otexts.com/fppit/prophet.html (fonte: Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3)


Se Knime ti segnala degli errori nell'esecuzione di un nodo Python Script ("Execution failed: Could not connect to the Python process"), verifica: 

Se usi Anaconda, puoi installare la maggior parte dei pacchetti da Conda. Da terminale ho dovuto installare solo nomkl (pip install nomkl). Per entrambe le installazioni puoi vedere https://docs.anaconda.com/anacondaorg/user-guide/packages/installing-packages/
A questo punto il nodo dovrebbe funzionare. :)


C'è anche una guida dedicata all'integrazione tra Knime e Python. Si chiama "KNIME Python Integration Guide" (ovviamente) e la trovi qui: https://docs.knime.com/latest/python_installation_guide/index.html#_introduction


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