import pandas as pd
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
import plotly.offline as py
from fbprophet.diagnostics import cross_validation
from fbprophet.diagnostics import performance_metrics
from fbprophet.plot import plot_cross_validation_metric
#import chart_studioTime series prediction is more complicated than I originally anticipated when I tackled the subject during my thesis - while you can treat the events independently, like geographic data, everything is related: what happened yesterday will affect what happened today, and a Friday in July is not the same as a Monday in October.
There are various weird and wonderful algorithms to cope with these complexities, but Facebook’s open source Prophet does a fantastic job of providing a “fire and forget” solution that just works.
This is the code extract from my Medium blog here.
Data Cleaning and Aggregating
We’ll be using four years of LAPD call data, aggregated to hourly intervals. Prophet actually copes with various intervals quite well, so don’t worry too much about how you do yours: just try and keep regular intervals, without too many missings bits.
df_by_hour = pd.read_csv("/content/total_calls_per_hour.csv")df_by_hour["ds"] = pd.date_range(min(df_by_hour["ds"]), max(df_by_hour["ds"]), freq='H')Your final cleaned data-set must contain the below two columns, ds and y. Everything else, Prophet will deal with.
df_by_hour.head()| Dispatch Date_Dispatch Time | call volume | ds | y | |
|---|---|---|---|---|
| 0 | 2015-01-01 00:00:00 | 286 | 2015-01-01 00:00:00 | 286 |
| 1 | 2015-01-01 01:00:00 | 265 | 2015-01-01 01:00:00 | 265 |
| 2 | 2015-01-01 02:00:00 | 179 | 2015-01-01 02:00:00 | 179 |
| 3 | 2015-01-01 03:00:00 | 152 | 2015-01-01 03:00:00 | 152 |
| 4 | 2015-01-01 04:00:00 | 127 | 2015-01-01 04:00:00 | 127 |
df_by_hour.tail()| Dispatch Date_Dispatch Time | call volume | ds | y | |
|---|---|---|---|---|
| 43819 | 2019-12-31 19:00:00 | 310 | 2019-12-31 19:00:00 | 310 |
| 43820 | 2019-12-31 20:00:00 | 320 | 2019-12-31 20:00:00 | 320 |
| 43821 | 2019-12-31 21:00:00 | 373 | 2019-12-31 21:00:00 | 373 |
| 43822 | 2019-12-31 22:00:00 | 354 | 2019-12-31 22:00:00 | 354 |
| 43823 | 2019-12-31 23:00:00 | 281 | 2019-12-31 23:00:00 | 281 |
##Fitting and Deploying Prophet works similar to most Python sklearn type implementations - just fit the data and you’re off.
Helpfully, it will also make you a data-frame containing future dates for you to predict on. It will also provide a breakdown of seasonality trends.
m = Prophet()
m.fit(df_by_hour)INFO:numexpr.utils:NumExpr defaulting to 2 threads.<fbprophet.forecaster.Prophet at 0x7fdbb146bdd8>future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)fig1 = m.plot(forecast)
fig2 = m.plot_components(forecast)
fig = plot_plotly(m, forecast) # This returns a plotly Figure
fig.show()Diagnostics and Cross Validation
Helpfully, Prophet also contains cross-validation functionality - and performs quite well, very quickly!
df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')
df_cv.head()INFO:fbprophet:Making 5 forecasts with cutoffs between 2017-01-10 23:00:00 and 2018-12-31 23:00:00| ds | yhat | yhat_lower | yhat_upper | y | cutoff | |
|---|---|---|---|---|---|---|
| 0 | 2017-01-11 00:00:00 | 116.692077 | 89.859738 | 142.382745 | 79 | 2017-01-10 23:00:00 |
| 1 | 2017-01-11 01:00:00 | 95.572727 | 69.135035 | 122.850000 | 71 | 2017-01-10 23:00:00 |
| 2 | 2017-01-11 02:00:00 | 71.996010 | 45.771390 | 98.558667 | 55 | 2017-01-10 23:00:00 |
| 3 | 2017-01-11 03:00:00 | 49.332357 | 23.467584 | 76.244978 | 51 | 2017-01-10 23:00:00 |
| 4 | 2017-01-11 04:00:00 | 33.137479 | 4.387625 | 60.912735 | 41 | 2017-01-10 23:00:00 |
df_p = performance_metrics(df_cv)
df_p.head()INFO:fbprophet:Skipping MAPE because y close to 0| horizon | mse | rmse | mae | mdape | coverage | |
|---|---|---|---|---|---|---|
| 0 | 36 days 12:00:00 | 2938.141411 | 54.204625 | 33.287400 | 0.167800 | 0.660046 |
| 1 | 36 days 13:00:00 | 2929.579619 | 54.125591 | 33.235735 | 0.167594 | 0.660731 |
| 2 | 36 days 14:00:00 | 2919.565265 | 54.033002 | 33.187913 | 0.167276 | 0.660959 |
| 3 | 36 days 15:00:00 | 2908.693562 | 53.932305 | 33.155190 | 0.166688 | 0.661187 |
| 4 | 36 days 16:00:00 | 2910.565179 | 53.949654 | 33.167673 | 0.166688 | 0.660959 |
fig = plot_cross_validation_metric(df_cv, metric='rmse')