Daily active users

import polars as pl
import matplotlib.pyplot as plt

from synforecast.generators import DailyActiveUsersGenerator

consumer_df = DailyActiveUsersGenerator(
    engine="polars", min_length=120, max_length=120, freq="D",
    base_users=10000.0, growth_rate=0.002, app_type="consumer", seed=42,
).generate(n_series=1)

business_df = DailyActiveUsersGenerator(
    engine="polars", min_length=120, max_length=120, freq="D",
    base_users=10000.0, growth_rate=0.002, app_type="business", seed=42,
).generate(n_series=1)

gaming_df = DailyActiveUsersGenerator(
    engine="polars", min_length=120, max_length=120, freq="D",
    base_users=10000.0, growth_rate=0.002, app_type="gaming", seed=42,
).generate(n_series=1)

panels = [
    ("consumer", consumer_df),
    ("business (B2B)", business_df),
    ("gaming", gaming_df),
]
fig, axes = plt.subplots(3, 1, figsize=(12, 7.5), sharex=True)
for ax, (label, df) in zip(axes, panels):
    ax.plot(df["ds"].to_list(), df["y"].to_list(), alpha=0.85, linewidth=1)
    ax.set(ylabel="Active users", title=label)
axes[-1].set_xlabel("Timestamp")
plt.tight_layout()
plt.show()

event_days = consumer_df.filter(pl.col("event") == 1)
if len(event_days) > 0:
    print("Days when events occurred:")
    print(event_days)

    avg_event_day = consumer_df.filter(pl.col("event") == 1)["y"].mean()
    avg_normal_day = consumer_df.filter(pl.col("event") == 0)["y"].mean()
    print(f"\nAverage DAU on event days: {avg_event_day:,.0f}")
    print(f"Average DAU on normal days: {avg_normal_day:,.0f}")
    print(f"Event day lift: {(avg_event_day/avg_normal_day - 1)*100:.1f}%")

Days when events occurred:
shape: (4, 4)
┌───────────┬─────────────────────┬──────────────┬───────┐
│ unique_id ┆ ds                  ┆ y            ┆ event │
│ ---       ┆ ---                 ┆ ---          ┆ ---   │
│ cat       ┆ datetime[ns]        ┆ f64          ┆ i32   │
╞═══════════╪═════════════════════╪══════════════╪═══════╡
│ 0         ┆ 2000-01-20 00:00:00 ┆ 10816.372729 ┆ 1     │
│ 0         ┆ 2000-03-22 00:00:00 ┆ 15008.853745 ┆ 1     │
│ 0         ┆ 2000-04-08 00:00:00 ┆ 21994.846532 ┆ 1     │
│ 0         ┆ 2000-04-20 00:00:00 ┆ 22292.652679 ┆ 1     │
└───────────┴─────────────────────┴──────────────┴───────┘

Average DAU on event days: 17,528
Average DAU on normal days: 12,089
Event day lift: 45.0%

hourly_params = {
    "min_length": 168,
    "max_length": 168,
    "freq": "h",
    "app_type": "consumer",
    "base_users": 10000.0,
    "event_probability": 0.01,
    "noise_std": 0.08,
    "seed": 42,
}
hourly_gen = DailyActiveUsersGenerator(engine="polars", **hourly_params)
hourly_df = hourly_gen.generate(n_series=1)

print(f"Generated {len(hourly_df)} hourly observations")
hourly_df.head(24)

Generated 168 hourly observations

fig, ax = plt.subplots(figsize=(12, 4))
ax.plot(hourly_df["ds"].to_list(), hourly_df["y"].to_list(), alpha=0.8, color="C4")
ax.set_xlabel("Timestamp")
ax.set_ylabel("Active Users")
ax.set_title("Hourly active users — consumer app (1 week)")
plt.tight_layout()
plt.show()

multi_params = {
    "min_length": 30,
    "max_length": 30,
    "freq": "D",
    "app_type": "consumer",
    "base_users": 20000.0,
    "event_probability": 0.03,
    "seed": 42,
}
multi_gen = DailyActiveUsersGenerator(engine="polars", **multi_params)
multi_df = multi_gen.generate(n_series=5)

print(f"Generated 5 products with {len(multi_df)} total observations")
print("\nProduct 0 preview:")
print(multi_df.filter(pl.col("unique_id") == "0").head(10))

total_events = multi_df.group_by("unique_id").agg(pl.col("event").sum())
print("\nEvents per product:")
print(total_events)

Generated 5 products with 150 total observations

Product 0 preview:
shape: (10, 4)
┌───────────┬─────────────────────┬──────────────┬───────┐
│ unique_id ┆ ds                  ┆ y            ┆ event │
│ ---       ┆ ---                 ┆ ---          ┆ ---   │
│ cat       ┆ datetime[ns]        ┆ f64          ┆ i32   │
╞═══════════╪═════════════════════╪══════════════╪═══════╡
│ 0         ┆ 2000-01-01 00:00:00 ┆ 19094.054486 ┆ 0     │
│ 0         ┆ 2000-01-02 00:00:00 ┆ 20350.825855 ┆ 0     │
│ 0         ┆ 2000-01-03 00:00:00 ┆ 19354.510065 ┆ 0     │
│ 0         ┆ 2000-01-04 00:00:00 ┆ 19688.625437 ┆ 0     │
│ 0         ┆ 2000-01-05 00:00:00 ┆ 20221.064661 ┆ 0     │
│ 0         ┆ 2000-01-06 00:00:00 ┆ 15882.965767 ┆ 0     │
│ 0         ┆ 2000-01-07 00:00:00 ┆ 17069.908021 ┆ 0     │
│ 0         ┆ 2000-01-08 00:00:00 ┆ 18599.75721  ┆ 0     │
│ 0         ┆ 2000-01-09 00:00:00 ┆ 21305.138222 ┆ 0     │
│ 0         ┆ 2000-01-10 00:00:00 ┆ 22202.888052 ┆ 0     │
└───────────┴─────────────────────┴──────────────┴───────┘

Events per product:
shape: (5, 2)
┌───────────┬───────┐
│ unique_id ┆ event │
│ ---       ┆ ---   │
│ cat       ┆ i32   │
╞═══════════╪═══════╡
│ 3         ┆ 1     │
│ 1         ┆ 1     │
│ 0         ┆ 1     │
│ 2         ┆ 1     │
│ 4         ┆ 1     │
└───────────┴───────┘

fig, ax = plt.subplots(figsize=(12, 4))
for uid in multi_df["unique_id"].unique().to_list():
    series = multi_df.filter(pl.col("unique_id") == uid)
    ax.plot(series["ds"].to_list(), series["y"].to_list(), label=uid, alpha=0.7)
ax.set_xlabel("Timestamp")
ax.set_ylabel("DAU")
ax.set_title("Multiple products — daily active users")
ax.legend()
plt.tight_layout()
plt.show()

custom_params = {
    "min_length": 30,
    "max_length": 30,
    "freq": "D",
    "base_users": 15000.0,
    "event_probability": 0.05,
    "event_col": "marketing_campaign",
    "id_col": "product_id",
    "time_col": "date",
    "target_col": "dau",
    "seed": 42,
}
custom_gen = DailyActiveUsersGenerator(engine="polars", **custom_params)
custom_df = custom_gen.generate(n_series=1)

print(f"Columns: {custom_df.columns}")
custom_df.head(10)

Columns: ['product_id', 'date', 'dau', 'marketing_campaign']