or regular time intervals DAX 30 Index - 27 Year Historical Chart Source: https://www.dwd.de/EN/weather/weather_climate_local/schleswig- holstein_hamburg/hamburg/_node.html Source: https://www.macrotrends.net/2595/dax-30-index-germany-historical -chart-data
(e.g., the quarter of the year, the month, or day of the week) Time series decomposition (for today’s intro to forecasting with Prophet) Trend: persistent increasing or decreasing direction in the data Residuals: random, irregular influences. It represents the remainder of the time series after the other components have been removed. t represents each period
Prophet follows the scikit-learn model API. We create an instance of the Prophet class and then call its fit and predict methods. Datestamp (pandas format) YYYY-MM-DD or YYYY-MM-DD HH:MM:SS Numeric: represents the measurement we wish to forecast
Yearly with Fourier series =10 • Weekly with Fourier series =3 • Increasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting • Seasonality_prior_scale can be adjusted to smooth the overfitting of the seasonality model
2013 to 2017 // 50 items per store, 10 stores Test.csv = 3 months into 2018 All items in all stores ➢ “Modified dataset”: We Focus on 1 store and 1 item and train from 2013 to September 2017 and test on October to December 2017.
repository with the complete tutorial $ git clone https://github.com/meiradania/forecast-with-prophet-intro 3. Create the conda environment in your workspace $ cd forecast-with-prophet-intro $ conda create --name prophet-intro --file spec-file.txt While the installation happens on the background…