"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/multiplicative_seasonality.md" between
prophet-1.1.tar.gz and prophet-1.1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

multiplicative_seasonality.md  (prophet-1.1):multiplicative_seasonality.md  (prophet-1.1.1)
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layout: docs layout: docs
docid: "multiplicative_seasonality" docid: "multiplicative_seasonality"
title: "Multiplicative Seasonality" title: "Multiplicative Seasonality"
permalink: /docs/multiplicative_seasonality.html permalink: /docs/multiplicative_seasonality.html
subsections: subsections:
--- ---
By default Prophet fits additive seasonalities, meaning the effect of the season ality is added to the trend to get the forecast. This time series of the number of air passengers is an example of when additive seasonality does not work: By default Prophet fits additive seasonalities, meaning the effect of the season ality is added to the trend to get the forecast. This time series of the number of air passengers is an example of when additive seasonality does not work:
```R ```R
# R # R
df <- read.csv('../examples/example_air_passengers.csv') df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples /example_air_passengers.csv')
m <- prophet(df) m <- prophet(df)
future <- make_future_dataframe(m, 50, freq = 'm') future <- make_future_dataframe(m, 50, freq = 'm')
forecast <- predict(m, future) forecast <- predict(m, future)
plot(m, forecast) plot(m, forecast)
``` ```
```python ```python
# Python # Python
df = pd.read_csv('../examples/example_air_passengers.csv') df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/exampl es/example_air_passengers.csv')
m = Prophet() m = Prophet()
m.fit(df) m.fit(df)
future = m.make_future_dataframe(50, freq='MS') future = m.make_future_dataframe(50, freq='MS')
forecast = m.predict(future) forecast = m.predict(future)
fig = m.plot(forecast) fig = m.plot(forecast)
``` ```
![png](/prophet/static/multiplicative_seasonality_files/multiplicative_seasonali ty_4_0.png) ![png](/prophet/static/multiplicative_seasonality_files/multiplicative_seasonali ty_4_0.png)
This time series has a clear yearly cycle, but the seasonality in the forecast i s too large at the start of the time series and too small at the end. In this ti me series, the seasonality is not a constant additive factor as assumed by Proph et, rather it grows with the trend. This is multiplicative seasonality. This time series has a clear yearly cycle, but the seasonality in the forecast i s too large at the start of the time series and too small at the end. In this ti me series, the seasonality is not a constant additive factor as assumed by Proph et, rather it grows with the trend. This is multiplicative seasonality.
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