Background in politics, commodity trading, and converted to being a data engineer in 2017. I worked with Django, Flask, Plotly, and Vue.JS, but now Airflow and PySpark for ETL pipelines.
Background in politics, commodity trading, and converted to being a data engineer in 2017. I worked with Django, Flask, Plotly, and Vue.JS, but now Airflow and PySpark for ETL pipelines.
There is something called the M-competitions where different techniques are used for times series forecasting and compared (en.wikipedia.org/wiki/Makridakis_C...). Traditional statistical models regularly outperform the Deep Thinking models.
Have you tried this with "traditional" models like ARIMA to compare? Obrigado!
not yet, who knows in the future with more data looking at more indicators, would you show me some way? I appreciate the idea, thank you :)
There are lots of examples with things like ARIMA. Give it a try! machinelearningmastery.com/arima-f...
There is something called the M-competitions where different techniques are used for times series forecasting and compared (en.wikipedia.org/wiki/Makridakis_C...). Traditional statistical models regularly outperform the Deep Thinking models.
Wow, very good, thank you very much for sharing; I will start studying this option; I do not promise anything for this year. =)