CODEWITHSUNDEEP
time-seriesforecastingforecastfacebook-prophet
rajes95
rajes95

Reputation: 105

FBProphet: Understanding Regressor Impact on Multivariate Forecast

Please see this example as the project I am working on is quite similar, but with ~8 regressors instead of 2 and I need to understand how each regressor is impacting the forecast model: https://towardsdatascience.com/forecast-model-tuning-with-additional-regressors-in-prophet-ffcbf1777dda

Given a scenario like above with 2 additional regressors: How can we understand the impact of each regressor on the 'yhat' forecast (ex. 'temp' has 30% impact on yhat prediction and 'weathersit' has 70% impact on yhat prediction or something similar) . I have tried using "from fbprophet.utilities import regressor_coefficients" to see regressor coefficients but I'm not sure if that's the right approach.

Additionally, how to interpret regressor columns in the 'forecast' dataframe from '.predict()'?

Thanks for your help.

Upvotes: 5

Views: 6037

Answers (2)

HungDo
HungDo

Reputation: 51

After running regressor_coefficients(model), you will get the center and coef of each additive regressor. For example:

regressor_coefficients(my_model)
|--|regressor| regressor_mode| center| coef_lower| coef| coef_upper|
|--|---------- |------------------|--------|------------|------|------------|
|0 |temperat|additive | 6.346457 | -51.124462| -51.124462| -51.124462|
|1 |humidity |additive | 66.665910| 7.736604| 7.736604| 7.736604|

So the results from your prediction should be (for additive seasonal trends):

yhat = trend + yearly + extra_regressors_additive,
where
extra_regressors_additive = (temperature_data - temperature_center)*temperature_coef
+ (humidity_data - humidity_center )* humidity_coef

Upvotes: 5

Jean-Philippe
Jean-Philippe

Reputation: 11

You can have more details about the regressors in the "forecast" dataframe. Look for the columns that represent your regressor name. If you feel that fbprophet is under estimating the impact of your regressor, you can declare your regressor input values as binary instead. You can also clusterize you regressor input values if binary values are not appropriate. If you still feel that your regressor is under estimated, have a look at you historical data of your regressor. Does the y value increase the same day your regressor behaviour change? If not then you need to fix that. You can also refer to the section "Coefficients of additional regressors" of this website: https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#additional-regressors

Upvotes: 1

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