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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| skipping to change at line 12 | | skipping to change at line 12 |
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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) |
| ``` | | ``` |
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|  | |  |
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| 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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