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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., & A...

People often pick standard models like ARIMA (Auto Regressive Integrated Moving Average), Exponential Smoothing, and Seasonal Decomposition of Time Series (STL) for time series analysis. They’re good because they’re easy to understand, sturdy against data assumptions, and based on strong theory. This is why they’re top choices for future predictions. As machine learning advances, we see a big increase in their use for predicting time series data. Tools like bagging and boosting stand out because they show great ability to spot tricky patterns and unusual links in time series data. Their unique feature is learning directly from raw data. They can deal with massive amounts of data and make accurate guesses. The following article is an experimental study that uses both statistical forecasts and machine learning-based forecasts to predict the future for a practical use case. The study then proceeds to compare the forecasts based on certain accuracy metrics in order to judge if the mach...

Since it is a challenging task to forecast highly anomalous and volatile data like crude price, this page says how to use a hybrid model for the same. ... The model is somewhat able to catch the volatility. But the point to keep in mind is that crude price is affected by many other factors mainly geopolitics. So, it is very difficult to get an accurate prediction of highly volatile and anomalous time series. But hybrid forecasting models may work better than other models since we are considering forecasting the volatility also. Leggi tutto:  https://ai.plainenglish.io/arima-garch-a-hybrid-model-to-forecast-highly-volatile-data-8b2ed0155b34