CODEWITHSUNDEEP
pythonforecastfacebook-prophet
Oscar Mayorga
Oscar Mayorga

Reputation: 69

Measure model accuracy on Prophet

I'm running this code. Forecasting for multiple time series with Prophet but don't know how to evaluate the model. 

import pandas as pd
from fbprophet import Prophet
data = pd.read_csv(r'C:\Users\XXX.csv')
ids = data['id'].unique()
series = []
for id in ids:
   f = data[data['id'] == id]
   series.append(f)

def run_prophet(timeserie):
    model = Prophet(yearly_seasonality=False,daily_seasonality=False)
    model.fit(timeserie)
    forecast = model.make_future_dataframe(periods=90, include_history=False)
    forecast = model.predict(forecast)
    return forecast

results = list(map(lambda timeserie: run_prophet(timeserie), series))

results[0] 
results[1]

The structure of the data is something like this:

id       ds         y
id1   2017-01-01    12
id2   2017-01-01    15
id3   2017-01-01    16

Upvotes: 1

Views: 3349

Answers (1)

Waqar Ahmad
Waqar Ahmad

Reputation: 92

you can do it by:

from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
r2_score(original price,predicted price)

this is same for the rest.

note: both arrays should have equal length of samples.

Upvotes: 1

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