| name | timeseries-multi-lag-target-features |
| description | Generate lag features for multiple targets over N days by shifting evaluation dates and self-joining per entity, creating a wide feature matrix of past target values |
Multi-Lag Target Features
Overview
For entity-level time-series forecasting with multiple targets, lagging each target by 1..N days and pivoting into separate columns creates a dense feature matrix capturing recent history. The self-join approach shifts the date column by the lag amount and merges back on (entity_id, date), producing columns like target1_1, target1_2, ..., target4_20 — 80 features for 4 targets x 20 lags.
Quick Start
from datetime import timedelta
TARGETS = ["target1", "target2", "target3", "target4"]
LAGS = list(range(1, 21))
def add_lag(df, lag):
lagged = df[["entity_id", "date"] + TARGETS].copy()
lagged["date"] = lagged["date"] + timedelta(days=lag)
df = df.merge(lagged, on=["entity_id", "date"], suffixes=("", f"_{lag}"), how="left")
return df
for lag in LAGS:
df = add_lag(df, lag)
lag_cols = [f"{t}_{l}" for l in reversed(LAGS) for t in TARGETS]
Workflow
- Start with a long-format DataFrame: one row per (entity_id, date)
- For each lag value, copy the target columns, shift dates forward by lag days
- Merge back on (entity_id, date) with a lag suffix
- Missing lags (start of series or gaps) become NaN — fill with 0 or entity median
- Use the lag columns as features for any model (GBDT, ANN, linear)
Key Decisions
- Lag range: 1-20 for daily data captures ~3 weeks of history; extend to 30+ for weekly patterns
- Offset: add an offset (e.g., 45 days) if targets aren't available until a delay after the evaluation date
- Fill strategy: fillna(0) is simple; entity-level median preserves scale differences
- Memory: for large datasets, compute lags in chunks or use
shift() within groups instead of merge
- At inference: maintain a rolling history buffer and pivot the last N days into lag columns
References
