Skip to content

April metaparameter updates #10

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
brendanwells-moz wants to merge 4 commits into
base: main
Choose a base branch
from
Open

April metaparameter updates #10

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
82aa9ee
Modify models for April forecast
brendanwells-moz Mar 24, 2026
46fe15c
Modify titles
brendanwells-moz Mar 24, 2026
4c3eff5
Clean up
brendanwells-moz Mar 27, 2026
4c61f9d
Warn if any holiday effects will be clipped at the -0.6 boundary
brendanwells-moz Mar 28, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 15 additions & 0 deletions mozaic/core.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import warnings

import numpy as np
import pandas as pd

Expand Down Expand Up @@ -291,6 +293,19 @@ def _predict_holiday_effects(self):
.sum()
.reindex(self.forecast_dates, fill_value=0)
)
# warn if any holiday effects will be clipped at the -0.6 boundary
clipped = self.proportional_holiday_effects[self.proportional_holiday_effects < -0.6]
if len(clipped) > 0:
details = ", ".join(
f"{date.strftime('%Y-%m-%d')} ({value:.3f})"
for date, value in clipped.items()
)
warnings.warn(
f"Holiday effects clipped at -0.6 boundary for dates: {details}",
UserWarning,
stacklevel=2,
)

# ensure no single-day impact becomes too large
self.proportional_holiday_effects.clip(lower=-0.6, upper=0, inplace=True)

Expand Down
43 changes: 13 additions & 30 deletions mozaic/models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,25 +10,19 @@ def desktop_forecast_model(historical_data, historical_dates, forecast_dates):
params = {
"daily_seasonality": False,
"weekly_seasonality": True,
"yearly_seasonality": False,
"yearly_seasonality": True,
"uncertainty_samples": 1000,
"changepoint_range": 0.8,
"changepoint_range": 0.7,
"seasonality_prior_scale": 0.00825,
"changepoint_prior_scale": 0.15983,
"growth": "logistic",
}

x = historical_data

# if x.max() >= 10e6:
# params["growth"] = "logistic"

if (x.abs().corr(x.diff().abs()) or 0) > 0.0:
params["seasonality_mode"] = "multiplicative"
params["growth"] = "linear"
# params["seasonality_prior_scale"] = 20
# params["changepoint_prior_scale"] = 0.05
# params["growth"] = "logistic"

if (len(x.dropna()) > (365 * 2)) and (
np.quantile(x.dropna(), 0.5) / (np.quantile(x.dropna(), 0.1) + 1e-8) < 5
Expand All @@ -49,25 +43,13 @@ def desktop_forecast_model(historical_data, historical_dates, forecast_dates):
)
future = pd.DataFrame({"ds": forecast_dates})

# if "growth" in params:
# if historical_data.max() >= 10e6:
# cap = observed["y"].max() * 1.2
# floor = observed["y"].min() * 0.8
# observed["cap"] = cap
# observed["floor"] = floor
# future["cap"] = cap
# future["floor"] = floor
# else:
# cap = observed["y"].max() * 1.2
# floor = 0.0
# observed["cap"] = cap
# observed["floor"] = floor
# future["cap"] = cap
# future["floor"] = floor

if params["growth"] == "logistic":
cap = observed["y"].max() * 1.2
floor = observed["y"].min() * 0.8
cap = observed["y"].tail(366).max() * 1.05
if cap > 100e6:
floor = observed["y"].tail(366).min() * 1
else:
floor = observed["y"].tail(366).min() * 0.92

observed["cap"] = cap
observed["floor"] = floor
future["cap"] = cap
Expand Down Expand Up @@ -96,13 +78,14 @@ def mobile_forecast_model(historical_data, historical_dates, forecast_dates):
"weekly_seasonality": True,
"yearly_seasonality": len(historical_data.dropna()) > (365 * 2),
"uncertainty_samples": 1000,
"changepoint_range": 0.8,
"changepoint_range": 0.82,
"growth": "logistic",
}

if historical_data.max() >= 1e6:
params["seasonality_prior_scale"] = 0.1
params["changepoint_prior_scale"] = 0.1
params["growth"] = "linear"

if historical_data.max() <= 2e6:
params["seasonality_mode"] = "multiplicative"
Expand All @@ -118,14 +101,14 @@ def mobile_forecast_model(historical_data, historical_dates, forecast_dates):

if "growth" in params:
if historical_data.max() >= 10e6:
cap = historical_data.max() * 2.0
floor = historical_data.min() * 0.8
cap = observed["y"].tail(366).max() * 1.10
floor = observed["y"].tail(366).min() * 1.05
observed["cap"] = cap
observed["floor"] = floor
future["cap"] = cap
future["floor"] = floor
else:
cap = historical_data.max() * 2.0
cap = historical_data.max() * 1.1
floor = 0.0
observed["cap"] = cap
observed["floor"] = floor
Expand Down