How to handle covid-19 shock in the fbprophet

[rajnish-garg]

Hi, I am using fbprophet on daily time series with 5 years of data. Due to this covid-19 pandemic most of our metrics are impacted. Because we are using last 20% of data for testing, so it is not able to capture the signals. Is there any recommendation on handling these issues.

[rbagd]

I think the general answer is that you actually can't. Your forecasting model assumes a level of stationarity in your series and for heavily impacted series Covid-19 shock just breaks it. Maybe a model with a regime change might help but we haven't got any past pandemics to learn from. Time series that I am working on all have their seasonalities broken with European population in lockdown, there's not much you can do other than maybe switch to a short-term ARIMA.

Although I believe it might be a lost cause to try to model anything in the short-term, I am wondering what strategies people might use to mitigate the shock after the pandemic. Just set pandemic period to missing data and hope for the best? Can we do better?

[bletham]

+1 for @rbagd's comment, Prophet assumes stationary seasonalities which is probably untrue for most human-related time series right now. In some areas there may be a long enough period of lockdown to check how stationary the seasonalities are within the lockdown period, and potentially fit a separate model just to that period. But there are so many frequent external shocks to the series (policy changes, etc.) that if things have stabilized, they probably won't stay stable for long.

As for after - I think we'll have to see what will end up working. Just throwing out the pandemic period as unuseful data could work but will really depend on what things look like after the pandemic clears up, and how similar they are to how things were before.

[benmwhite]

Just throwing out the pandemic period as unuseful data could work but will really depend on what things look like after the pandemic clears up

@bletham Or input the quarantine/lockdown periods as "holidays" in the model for full dark irony

[mik1893]

I guess since the covid-19 pandemic has numerous impacts which also vary on the nature of what you are trying to forecast and the business related to it...
it's pretty much impossible to define a standard set of rules to insert in a statistical modelling library...

[lazaronixon]

great results here https://medium.com/@andrejusb/covid-19-growth-modeling-and-forecasting-with-prophet-2ff5ebd00c01 I used this model on my application https://profetadocorona.herokuapp.com

[bletham]

I had a discussion with some other forecasters on this question last week, and one thing that came up that I wanted to mention for other people trying to salvage their forecasts is that you an specify different seasonalities for different periods of time, in particular here it is possible to have e.g. weekly seasonalities that are different "pre-corona" and "during-corona", like here: https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#seasonalities-that-depend-on-other-factors . That could be helpful for some time series.

[yuzuhikorunrun]

@bletham Hello Ben, thanks for the answer. One question though, in retail the corona shock is always playing negative on the sales, however by adding the pre-corona and during-corona seasonalities, prophet seems to mistakenly think during-corona, the sales peaks fly high.

Not sure if I am doing this righ, but can you please weightin here? Thank you!

Here're my parameters:

def is_Not_corona_period(ds):
    date = pd.to_datetime(ds)
    return (date.year != 2020)

temp['post_corona'] = ~temp['ds'].apply(is_Not_corona_period)
temp['pre_corona'] = temp['ds'].apply(is_Not_corona_period)

m3 = Prophet(holidays=holidays,
             interval_width = 0.99, # default is 80.
             holidays_prior_scale=0.25,
             changepoint_prior_scale=0.5,
             seasonality_mode='multiplicative', 
             yearly_seasonality=10, 
             weekly_seasonality=False, 
             daily_seasonality=False)

m3.add_seasonality(name='annually-pre-corona', 
                   period=365, 
                   fourier_order=10, 
                   condition_name='pre_corona')

m3.add_seasonality(name='annually-post-corona',
                   period=365, 
                   fourier_order=10, 
                   condition_name='post_corona')

and the resulting flyhigh:

[shoaibkhanz]

@bletham I want to throw an idea into this mix, which I have implemented in linear regression models I maintain at work. Now this isn't using Prophet but simple linear regression but I believe this can be extended.

I am using log-log linear regression model to estimate price elasticities (didnt use ARIMAX as that might reduce the price elasticity effect) of customers and then predict into the future, since the covid period my models deteriorated considerably. The challenge I had was that training another set models for covid would mean going back to model risk and agree on the coefficients(price elasticities) and the impacts it will have also not to forget feature selection.

Thus, since I couldnt change coefficients, I introduced moving average into my linear regression models (an idea borrowed from Time Series), what this means is that I am adjusting my intercept after fitting my model.

here are the steps I went through:

• Step1: Fit a Linear Regression model
• Step2: Calculate the Errors
• Step3: Calculate a rolling mean of 2 or 3 or 4 weeks (depends on the use case).
• Step4: Lag the calculated mean by 1 or 2 weeks (depends on the use case).
• Step5: Convert NAs to 0
• Step6: Add the error mean to the Step1

I have models in R thus here is the code of what I did.

  train_error = train_actual - train_preds_
  test_error = test_actual - test_preds_
  
  train_preds_ = train_preds_ + lag(as.vector(rollmean(train_error,k = 2,fill = FALSE,align = 'right')),default = 0)
  test_preds_ = test_preds_ + lag(as.vector(rollmean(test_error,k = 2,fill = FALSE,align = 'right')),default = 0)

What do you think about this approach ?
This has improved my models performance considerably as it’s adapting and learning from its past errors.

[bletham]

@yuzuhikorunrun the problem there is that you're fitting a yearly seasonality (annually-post-corona) with quite a bit less than a year of data. The 2020 seasonality for June on is thus totally unconstrained, and in this case is blowing up in a really bad way. That's because by default there is very little regularization on the fitted seasonalities. You could specify the prior_scale in add_seasonality to something small like 0.1 which would clamp down on that resonance, but really it probably doesn't make sense to try to predict yearly seasonality for 2020 distinctly from previous years. In my earlier post I was thinking more like weekly seasonality, where we do have multiple weeks of during-corona and can reasonably fit the seasonality.

[bletham]

@shoaibkhanz I guess what you're doing is along the lines of fitting a model to the residuals (in this case the model is a rolling mean). That makes a lot of sense, thanks for sharing!

[shoaibkhanz]

Thanks @bletham , I am glad that you found that useful.

[yuzuhikorunrun]

@bletham Hello thank you for the prompt reply, I appreciate this. Wondering if I can set it as monthly seasonality since my data are monthly data and I only have Jan-March upon this time. thank you!!!

[bletham]

@yuzuhikorunrun Monthly seasonality would mean a cycle within a month, and so wouldn't be appropriate here. Unfortunately with monthly data I don't think there is a whole lot that can be learned with just a few months of data. You'll have to let the trend component capture the chang due to COVID; because it is a change right at the end of the time series, you might need to increase the changepoint_range to something like 0.95 (see https://facebook.github.io/prophet/docs/trend_changepoints.html for more details).

[yuzuhikorunrun]

@bletham Unfortunately, setting changepoint_range to 0.95 or 0.9 does not improve the model performance and it failed to capture the sudden decreased sales due to COVID shock in Feb and March (not so much impact in Jan). I do have some interesting (or just lucky) findings and I'd love to hear your thoughts on this.

I have monthly data up to March-2020, and I mistakenly added monthly seasonality to my parameters (my data has multiplicative yearly trend)

def is_corona_period(ds):
    date = pd.to_datetime(ds)
    return (date.year == 2020)

temp['post_corona'] = temp['ds'].apply(is_corona_period)
temp['pre_corona'] = ~temp['ds'].apply(is_corona_period)

m5.add_seasonality(name='monthly-pre-corona', 
                   period=30, 
                   prior_scale = 0.1,
                   fourier_order=10, 
                   condition_name='pre_corona')

m5.add_seasonality(name='monthly-post-corona',
                   period=30, 
                   prior_scale = 0.1,
                   fourier_order=10,
                   condition_name='post_corona')

then, boom, it actually performs better in predicting the Feb and March data (after adding Jan-2020 data to train):

Before-Adding-wrong-seasonlity


Also notice here my extra regressor's impact is positive which is expected.

beta-parameters:

After-Adding-wrong-seasonality



Notice my extra-regressor here plays negative instead which is not expected...

a quick comparison with true-value, predict-values.

beta-parameters:

Do you think this is purely luck? And any idea of what might be going on here to make this lucky improvement happen?

Thank you again for making this amazing tool.

-Best.