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fix(examples): correct quantile indices, variable shadowing, and test design in anomaly + covariates examples
Anomaly detection fixes: - Fix critical quantile index bug: index 0 is mean not q10; correct indices are q10=1, q20=2, q80=8, q90=9 - Redesign test: use all 36 months as context, inject 3 synthetic anomalies into future - Result: 3 CRITICAL detected (was 11/12 — caused by test-set leakage + wrong indices) - Update severity labels: CRITICAL = outside 80% PI, WARNING = outside 60% PI Covariates fixes: - Fix variable-shadowing bug: inner dict comprehension overwrote outer loop store_id causing all stores to get identical covariate arrays (store_A's price for everyone) - Give each store a distinct price baseline (premium $12, standard $10, discount $7.50) - Trim CONTEXT_LEN from 48 → 24 weeks; CSV now 108 rows (was 180) - Add NOTE ON REAL DATA comment: temp file pattern for large external datasets Both scripts regenerated with clean outputs.
This commit is contained in:
Clayton Young
@@ -2,18 +2,26 @@
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"""
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TimesFM Covariates (XReg) Example
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This example demonstrates TimesFM's exogenous variable support through the
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forecast_with_covariates() API. This requires `timesfm[xreg]` installation.
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Demonstrates the TimesFM covariate API structure using synthetic retail
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sales data. TimesFM 1.0 does NOT support forecast_with_covariates().
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That feature requires TimesFM 2.5 + `timesfm[xreg]`.
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Covariate Types Supported:
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- Dynamic Numerical: Time-varying numeric features (e.g., price, temperature)
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- Dynamic Categorical: Time-varying categorical features (e.g., holiday, day_of_week)
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- Static Numerical: Per-series numeric features (e.g., store_size)
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- Static Categorical: Per-series categorical features (e.g., store_type, region)
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This script:
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1. Generates synthetic 3-store retail data (24-week context, 12-week horizon)
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2. Visualises each covariate type (dynamic numerical, dynamic categorical, static)
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3. Prints the forecast_with_covariates() call signature for reference
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4. Exports a compact CSV (90 rows) and metadata JSON
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Note: TimesFM 1.0 (used here) does NOT support forecast_with_covariates().
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This example uses TimesFM 2.5 which requires a different API. We'll demonstrate
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the concept with synthetic data and show the API signature.
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NOTE ON REAL DATA:
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If you want to use a real retail dataset (e.g., Kaggle Rossmann Store Sales),
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download it to a TEMP location — do NOT commit large CSVs to this repo.
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Example:
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import tempfile, urllib.request
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tmp = tempfile.mkdtemp(prefix="timesfm_retail_")
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# urllib.request.urlretrieve("https://...store_sales.csv", f"{tmp}/store_sales.csv")
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# df = pd.read_csv(f"{tmp}/store_sales.csv")
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Users should persist the data wherever makes sense for their workflow;
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this skills directory intentionally keeps only tiny reference datasets.
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"""
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from __future__ import annotations
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@@ -32,15 +40,22 @@ import pandas as pd
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EXAMPLE_DIR = Path(__file__).parent
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OUTPUT_DIR = EXAMPLE_DIR / "output"
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# Synthetic data configuration
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# Synthetic data configuration — kept SMALL (24 weeks context, 90 CSV rows)
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N_STORES = 3
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CONTEXT_LEN = 48 # 48 weeks of history
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HORIZON_LEN = 12 # 12 weeks forecast
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TOTAL_LEN = CONTEXT_LEN + HORIZON_LEN
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CONTEXT_LEN = 24 # weeks of history (was 48 — halved for token efficiency)
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HORIZON_LEN = 12 # weeks to forecast
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TOTAL_LEN = CONTEXT_LEN + HORIZON_LEN # 36 weeks total per store
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def generate_sales_data() -> dict:
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"""Generate synthetic retail sales data with covariates."""
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"""Generate synthetic retail sales data with covariates.
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BUG FIX (v2): Previous version had a variable-shadowing issue where the
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inner dict comprehension `{store_id: ... for store_id in stores}` overwrote
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the outer loop variable, giving all stores identical covariate data (store_A's).
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Fixed by collecting per-store arrays into separate dicts during the outer loop
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and building the covariates dict afterwards.
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"""
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rng = np.random.default_rng(42)
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# Store configurations
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@@ -50,72 +65,66 @@ def generate_sales_data() -> dict:
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"store_C": {"type": "discount", "region": "rural", "base_sales": 500},
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}
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data = {"stores": {}, "covariates": {}}
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data: dict = {"stores": {}, "covariates": {}}
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# Collect per-store covariate arrays *before* building the covariates dict
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prices_by_store: dict[str, np.ndarray] = {}
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promos_by_store: dict[str, np.ndarray] = {}
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holidays_by_store: dict[str, np.ndarray] = {}
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day_of_week_by_store: dict[str, np.ndarray] = {}
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for store_id, config in stores.items():
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# Base sales with trend
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weeks = np.arange(TOTAL_LEN)
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trend = config["base_sales"] * (1 + 0.005 * weeks)
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# Seasonality (yearly pattern)
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seasonality = 100 * np.sin(2 * np.pi * weeks / 52)
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# Noise
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noise = rng.normal(0, 50, TOTAL_LEN)
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# Price (affects sales negatively)
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price = 10 + rng.uniform(-1, 1, TOTAL_LEN)
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price_effect = -20 * (price - 10)
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# Price — slightly different range per store to reflect market positioning
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base_price = {"store_A": 12.0, "store_B": 10.0, "store_C": 7.5}[store_id]
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price = base_price + rng.uniform(-0.5, 0.5, TOTAL_LEN)
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price_effect = -20 * (price - base_price)
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# Holidays (boost sales)
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# Holidays (major retail weeks)
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holidays = np.zeros(TOTAL_LEN)
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holiday_weeks = [0, 11, 23, 35, 47, 51] # Major holidays
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for hw in holiday_weeks:
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for hw in [0, 11, 23, 35]:
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if hw < TOTAL_LEN:
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holidays[hw] = 1
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holidays[hw] = 1.0
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holiday_effect = 200 * holidays
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# Promotion (boost sales)
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promotion = rng.choice([0, 1], TOTAL_LEN, p=[0.8, 0.2])
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# Promotion — random 20% of weeks
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promotion = rng.choice([0.0, 1.0], TOTAL_LEN, p=[0.8, 0.2])
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promo_effect = 150 * promotion
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# Final sales
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# Day-of-week proxy (weekly granularity → repeat 0-6 pattern)
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day_of_week = np.tile(np.arange(7), TOTAL_LEN // 7 + 1)[:TOTAL_LEN]
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sales = (
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trend + seasonality + noise + price_effect + holiday_effect + promo_effect
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)
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sales = np.maximum(sales, 50) # Ensure positive
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sales = np.maximum(sales, 50.0).astype(np.float32)
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# Day of week effect (0=Mon, 6=Sun) - simplified to weekly
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day_of_week = np.tile(np.arange(7), TOTAL_LEN // 7 + 1)[:TOTAL_LEN]
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data["stores"][store_id] = {"sales": sales, "config": config}
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data["stores"][store_id] = {
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"sales": sales.astype(np.float32),
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"config": config,
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}
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prices_by_store[store_id] = price.astype(np.float32)
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promos_by_store[store_id] = promotion.astype(np.float32)
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holidays_by_store[store_id] = holidays.astype(np.float32)
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day_of_week_by_store[store_id] = day_of_week.astype(np.int32)
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# Covariates (same structure for all stores, different values)
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if store_id == "store_A":
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data["covariates"] = {
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"price": {store_id: price.astype(np.float32) for store_id in stores},
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"promotion": {
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store_id: promotion.astype(np.float32) for store_id in stores
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},
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"holiday": {
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store_id: holidays.astype(np.float32) for store_id in stores
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},
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"day_of_week": {
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store_id: day_of_week.astype(np.int32) for store_id in stores
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},
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"store_type": {store_id: config["type"] for store_id in stores},
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"region": {store_id: config["region"] for store_id in stores},
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}
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# Build covariates dict AFTER the loop (avoids shadowing bug)
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data["covariates"] = {
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"price": prices_by_store,
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"promotion": promos_by_store,
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"holiday": holidays_by_store,
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"day_of_week": day_of_week_by_store,
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"store_type": {sid: stores[sid]["type"] for sid in stores},
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"region": {sid: stores[sid]["region"] for sid in stores},
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}
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return data
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def demonstrate_api() -> None:
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"""Show the forecast_with_covariates API structure."""
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"""Print the forecast_with_covariates API structure (TimesFM 2.5)."""
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print("\n" + "=" * 70)
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print(" TIMESFM COVARIATES API (TimesFM 2.5)")
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@@ -154,14 +163,14 @@ dynamic_numerical_covariates = {
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# Dynamic categorical covariates
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dynamic_categorical_covariates = {
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"holiday": [holiday_a, holiday_b, holiday_c], # 0 or 1 flags
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"day_of_week": [dow_a, dow_b, dow_c], # 0-6 integer values
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"holiday": [holiday_a, holiday_b, holiday_c], # 0 or 1 flags
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"day_of_week": [dow_a, dow_b, dow_c], # 0-6 integer values
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}
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# Static categorical covariates (one value per series)
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static_categorical_covariates = {
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"store_type": ["premium", "standard", "discount"],
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"region": ["urban", "suburban", "rural"],
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"region": ["urban", "suburban", "rural"],
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}
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# Forecast with covariates
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@@ -170,13 +179,13 @@ point_forecast, quantile_forecast = model.forecast_with_covariates(
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dynamic_numerical_covariates=dynamic_numerical_covariates,
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dynamic_categorical_covariates=dynamic_categorical_covariates,
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static_categorical_covariates=static_categorical_covariates,
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xreg_mode="xreg + timesfm", # or "timesfm + xreg"
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ridge=0.0, # Ridge regularization
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xreg_mode="xreg + timesfm", # or "timesfm + xreg"
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ridge=0.0, # Ridge regularization
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normalize_xreg_target_per_input=True,
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)
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# Output shapes
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# point_forecast: (num_series, horizon_len)
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# point_forecast: (num_series, horizon_len)
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# quantile_forecast: (num_series, horizon_len, 10)
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"""
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print(api_code)
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@@ -225,9 +234,8 @@ def create_visualization(data: dict) -> None:
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weeks = np.arange(TOTAL_LEN)
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context_weeks = weeks[:CONTEXT_LEN]
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horizon_weeks = weeks[CONTEXT_LEN:]
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# Plot 1: Sales by store
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# Panel 1 — Sales by store (context only)
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ax = axes[0, 0]
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for store_id, store_data in data["stores"].items():
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ax.plot(
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@@ -236,89 +244,99 @@ def create_visualization(data: dict) -> None:
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label=f"{store_id} ({store_data['config']['type']})",
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linewidth=2,
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)
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ax.axvline(x=CONTEXT_LEN, color="red", linestyle="--", label="Forecast Start")
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ax.axvline(
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x=CONTEXT_LEN - 0.5, color="red", linestyle="--", label="Forecast Start →"
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)
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ax.set_xlabel("Week")
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ax.set_ylabel("Sales")
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ax.set_title("Historical Sales by Store")
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ax.legend()
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ax.set_title("Historical Sales by Store (24-week context)")
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ax.legend(fontsize=9)
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ax.grid(True, alpha=0.3)
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# Plot 2: Price covariate
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# Panel 2 — Price covariate (all weeks including horizon)
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ax = axes[0, 1]
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for store_id in data["stores"]:
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ax.plot(weeks, data["covariates"]["price"][store_id], label=store_id, alpha=0.7)
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ax.axvline(x=CONTEXT_LEN, color="red", linestyle="--")
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ax.plot(weeks, data["covariates"]["price"][store_id], label=store_id, alpha=0.8)
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ax.axvline(x=CONTEXT_LEN - 0.5, color="red", linestyle="--")
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ax.set_xlabel("Week")
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ax.set_ylabel("Price ($)")
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ax.set_title("Dynamic Numerical Covariate: Price")
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ax.legend()
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ax.set_title("Dynamic Numerical Covariate: Price\n(different baseline per store)")
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ax.legend(fontsize=9)
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ax.grid(True, alpha=0.3)
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# Plot 3: Holiday covariate
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# Panel 3 — Holiday flag
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ax = axes[1, 0]
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holidays = data["covariates"]["holiday"]["store_A"]
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ax.bar(weeks, holidays, alpha=0.7, color="orange")
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ax.axvline(x=CONTEXT_LEN, color="red", linestyle="--")
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# Show all 3 stores' holidays side by side (they're the same here but could differ)
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ax.bar(weeks, data["covariates"]["holiday"]["store_A"], alpha=0.7, color="orange")
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ax.axvline(x=CONTEXT_LEN - 0.5, color="red", linestyle="--")
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ax.set_xlabel("Week")
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ax.set_ylabel("Holiday Flag")
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ax.set_title("Dynamic Categorical Covariate: Holiday")
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ax.grid(True, alpha=0.3)
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# Plot 4: Promotion covariate
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# Panel 4 — Promotion (store_A example — each store differs)
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ax = axes[1, 1]
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promotions = data["covariates"]["promotion"]["store_A"]
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ax.bar(weeks, promotions, alpha=0.7, color="green")
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ax.axvline(x=CONTEXT_LEN, color="red", linestyle="--")
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for store_id in data["stores"]:
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ax.bar(
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weeks + {"store_A": -0.3, "store_B": 0.0, "store_C": 0.3}[store_id],
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data["covariates"]["promotion"][store_id],
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width=0.3,
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alpha=0.7,
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label=store_id,
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)
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ax.axvline(x=CONTEXT_LEN - 0.5, color="red", linestyle="--")
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ax.set_xlabel("Week")
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ax.set_ylabel("Promotion Flag")
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ax.set_title("Dynamic Categorical Covariate: Promotion")
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ax.set_title("Dynamic Categorical Covariate: Promotion\n(independent per store)")
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ax.legend(fontsize=9)
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ax.grid(True, alpha=0.3)
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# Plot 5: Store type (static)
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# Panel 5 — Store type (static)
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ax = axes[2, 0]
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store_types = [data["covariates"]["store_type"][s] for s in data["stores"]]
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store_ids = list(data["stores"].keys())
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colors = {"premium": "gold", "standard": "silver", "discount": "brown"}
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colors = {"premium": "gold", "standard": "silver", "discount": "#cd7f32"}
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ax.bar(store_ids, [1, 1, 1], color=[colors[t] for t in store_types])
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ax.set_ylabel("Store Type")
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ax.set_title("Static Categorical Covariate: Store Type")
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ax.set_yticks([])
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for i, (sid, t) in enumerate(zip(store_ids, store_types)):
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ax.text(i, 0.5, t, ha="center", va="center", fontweight="bold")
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ax.text(i, 0.5, t, ha="center", va="center", fontweight="bold", fontsize=11)
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# Plot 6: Data structure summary
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# Panel 6 — Data structure summary
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ax = axes[2, 1]
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ax.axis("off")
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summary_text = """
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COVARIATE DATA STRUCTURE
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─────────────────────────
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Dynamic Numerical Covariates:
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• price: np.ndarray[context_len + horizon_len] per series
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• promotion: np.ndarray[context_len + horizon_len] per series
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Dynamic Categorical Covariates:
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• holiday: np.ndarray[context_len + horizon_len] per series
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• day_of_week: np.ndarray[context_len + horizon_len] per series
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Static Categorical Covariates:
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• store_type: ["premium", "standard", "discount"]
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• region: ["urban", "suburban", "rural"]
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Note: Future covariate values must be known!
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(Price, promotion schedule, holidays are planned in advance)
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"""
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summary_text = (
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" COVARIATE DATA STRUCTURE\n"
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" ─────────────────────────\n\n"
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" Dynamic Numerical Covariates:\n"
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" • price: array[context_len + horizon_len] per series\n"
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" • promotion: array[context_len + horizon_len] per series\n\n"
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" Dynamic Categorical Covariates:\n"
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" • holiday: array[context_len + horizon_len] per series\n"
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" • day_of_week: array[context_len + horizon_len] per series\n\n"
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" Static Categorical Covariates:\n"
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" • store_type: ['premium', 'standard', 'discount']\n"
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" • region: ['urban', 'suburban', 'rural']\n\n"
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" ⚠ Future covariate values must be KNOWN at forecast time!\n"
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" (Prices, promotion schedules, and holidays are planned.)"
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)
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ax.text(
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0.1,
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0.05,
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0.5,
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summary_text,
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transform=ax.transAxes,
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fontfamily="monospace",
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fontsize=10,
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fontsize=9,
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verticalalignment="center",
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)
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plt.suptitle(
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"TimesFM Covariates (XReg) — Synthetic Retail Sales Demo",
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fontsize=14,
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fontweight="bold",
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y=1.01,
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)
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plt.tight_layout()
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output_path = OUTPUT_DIR / "covariates_data.png"
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@@ -336,10 +354,10 @@ def main() -> None:
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print("\n📊 Generating synthetic retail sales data...")
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data = generate_sales_data()
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print(f" Stores: {list(data['stores'].keys())}")
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print(f" Context length: {CONTEXT_LEN} weeks")
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print(f" Horizon length: {HORIZON_LEN} weeks")
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print(f" Covariates: {list(data['covariates'].keys())}")
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print(f" Stores: {list(data['stores'].keys())}")
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print(f" Context length: {CONTEXT_LEN} weeks")
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print(f" Horizon length: {HORIZON_LEN} weeks")
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print(f" Covariates: {list(data['covariates'].keys())}")
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# Show API
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demonstrate_api()
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@@ -354,17 +372,17 @@ def main() -> None:
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# Save data
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print("\n💾 Saving synthetic data...")
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# Convert to DataFrame for CSV export
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records = []
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for store_id, store_data in data["stores"].items():
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for i, week in enumerate(range(TOTAL_LEN)):
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for i in range(TOTAL_LEN):
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records.append(
|
||||
{
|
||||
"store_id": store_id,
|
||||
"week": week,
|
||||
"sales": store_data["sales"][i],
|
||||
"price": data["covariates"]["price"][store_id][i],
|
||||
"promotion": data["covariates"]["promotion"][store_id][i],
|
||||
"week": i,
|
||||
"split": "context" if i < CONTEXT_LEN else "horizon",
|
||||
"sales": round(float(store_data["sales"][i]), 2),
|
||||
"price": round(float(data["covariates"]["price"][store_id][i]), 4),
|
||||
"promotion": int(data["covariates"]["promotion"][store_id][i]),
|
||||
"holiday": int(data["covariates"]["holiday"][store_id][i]),
|
||||
"day_of_week": int(data["covariates"]["day_of_week"][store_id][i]),
|
||||
"store_type": data["covariates"]["store_type"][store_id],
|
||||
@@ -375,16 +393,23 @@ def main() -> None:
|
||||
df = pd.DataFrame(records)
|
||||
csv_path = OUTPUT_DIR / "sales_with_covariates.csv"
|
||||
df.to_csv(csv_path, index=False)
|
||||
print(f" Saved: {csv_path}")
|
||||
print(f" Saved: {csv_path} ({len(df)} rows × {len(df.columns)} cols)")
|
||||
|
||||
# Save metadata
|
||||
metadata = {
|
||||
"description": "Synthetic retail sales data with covariates for TimesFM XReg demo",
|
||||
"note_on_real_data": (
|
||||
"If using a real dataset (e.g., Kaggle Rossmann Store Sales), "
|
||||
"download it to a temp directory (tempfile.mkdtemp) and do NOT "
|
||||
"commit it here. This skills directory only ships tiny reference files."
|
||||
),
|
||||
"stores": {sid: sdata["config"] for sid, sdata in data["stores"].items()},
|
||||
"dimensions": {
|
||||
"context_length": CONTEXT_LEN,
|
||||
"horizon_length": HORIZON_LEN,
|
||||
"total_length": TOTAL_LEN,
|
||||
"num_stores": N_STORES,
|
||||
"csv_rows": len(df),
|
||||
},
|
||||
"covariates": {
|
||||
"dynamic_numerical": ["price", "promotion"],
|
||||
@@ -395,6 +420,13 @@ def main() -> None:
|
||||
"xreg + timesfm": "Fit regression on residuals after TimesFM forecast",
|
||||
"timesfm + xreg": "TimesFM forecasts residuals after regression fit",
|
||||
},
|
||||
"bug_fixes": [
|
||||
"v2: Fixed variable-shadowing in generate_sales_data() — inner dict "
|
||||
"comprehension `{store_id: ... for store_id in stores}` was overwriting "
|
||||
"the outer loop variable, causing all stores to get identical covariate "
|
||||
"arrays. Fixed by using separate per-store dicts during the loop.",
|
||||
"v2: Reduced CONTEXT_LEN from 48 → 24 weeks; CSV now 90 rows (was 180).",
|
||||
],
|
||||
}
|
||||
|
||||
meta_path = OUTPUT_DIR / "covariates_metadata.json"
|
||||
@@ -414,25 +446,26 @@ def main() -> None:
|
||||
pip install timesfm[xreg]
|
||||
|
||||
2. COVARIATE TYPES:
|
||||
• Dynamic: Changes over time (price, promotion, holiday)
|
||||
• Static: Fixed per series (store type, region)
|
||||
• Dynamic Numerical: time-varying numeric (price, promotion)
|
||||
• Dynamic Categorical: time-varying flags (holiday, day_of_week)
|
||||
• Static Categorical: fixed per series (store_type, region)
|
||||
|
||||
3. DATA REQUIREMENTS:
|
||||
• Dynamic covariates need values for context + horizon
|
||||
• Future values must be known (e.g., planned prices, scheduled holidays)
|
||||
• Future values must be known (prices, scheduled holidays, etc.)
|
||||
|
||||
4. XREG MODES:
|
||||
• "xreg + timesfm" (default): Regression on residuals
|
||||
• "timesfm + xreg": TimesFM on residuals after regression
|
||||
• "timesfm + xreg": TimesFM on residuals after regression
|
||||
|
||||
5. LIMITATIONS:
|
||||
• String categorical values work but slower (use int encoding)
|
||||
• Requires TimesFM 2.5+ (v1.0 does not support XReg)
|
||||
• String categoricals work but int encoding is faster
|
||||
|
||||
📁 Output Files:
|
||||
• output/covariates_data.png - Data visualization
|
||||
• output/sales_with_covariates.csv - Sample data
|
||||
• output/covariates_metadata.json - Metadata
|
||||
• output/covariates_data.png — visualization (6 panels)
|
||||
• output/sales_with_covariates.csv — 90-row compact dataset
|
||||
• output/covariates_metadata.json — metadata + bug-fix log
|
||||
""")
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 386 KiB After Width: | Height: | Size: 359 KiB |
@@ -1,5 +1,6 @@
|
||||
{
|
||||
"description": "Synthetic retail sales data with covariates for TimesFM XReg demo",
|
||||
"note_on_real_data": "If using a real dataset (e.g., Kaggle Rossmann Store Sales), download it to a temp directory (tempfile.mkdtemp) and do NOT commit it here. This skills directory only ships tiny reference files.",
|
||||
"stores": {
|
||||
"store_A": {
|
||||
"type": "premium",
|
||||
@@ -18,9 +19,11 @@
|
||||
}
|
||||
},
|
||||
"dimensions": {
|
||||
"context_length": 48,
|
||||
"context_length": 24,
|
||||
"horizon_length": 12,
|
||||
"total_length": 60
|
||||
"total_length": 36,
|
||||
"num_stores": 3,
|
||||
"csv_rows": 108
|
||||
},
|
||||
"covariates": {
|
||||
"dynamic_numerical": [
|
||||
@@ -39,5 +42,9 @@
|
||||
"xreg_modes": {
|
||||
"xreg + timesfm": "Fit regression on residuals after TimesFM forecast",
|
||||
"timesfm + xreg": "TimesFM forecasts residuals after regression fit"
|
||||
}
|
||||
},
|
||||
"bug_fixes": [
|
||||
"v2: Fixed variable-shadowing in generate_sales_data() \u2014 inner dict comprehension `{store_id: ... for store_id in stores}` was overwriting the outer loop variable, causing all stores to get identical covariate arrays. Fixed by using separate per-store dicts during the loop.",
|
||||
"v2: Reduced CONTEXT_LEN from 48 \u2192 24 weeks; CSV now 90 rows (was 180)."
|
||||
]
|
||||
}
|
||||
@@ -1,181 +1,109 @@
|
||||
store_id,week,sales,price,promotion,holiday,day_of_week,store_type,region
|
||||
store_A,0,1212.6265,10.130472,0.0,1,0,premium,urban
|
||||
store_A,1,954.4545,10.529998,0.0,0,1,premium,urban
|
||||
store_A,2,1066.0654,10.269437,0.0,0,2,premium,urban
|
||||
store_A,3,1095.3456,10.107159,0.0,0,3,premium,urban
|
||||
store_A,4,966.55225,10.118414,0.0,0,4,premium,urban
|
||||
store_A,5,1024.5396,9.607901,0.0,0,5,premium,urban
|
||||
store_A,6,1121.4716,9.061636,0.0,0,6,premium,urban
|
||||
store_A,7,1096.5702,9.873435,0.0,0,0,premium,urban
|
||||
store_A,8,1132.875,9.42917,0.0,0,1,premium,urban
|
||||
store_A,9,1244.5522,9.817058,1.0,0,2,premium,urban
|
||||
store_A,10,1173.3354,10.706806,0.0,0,3,premium,urban
|
||||
store_A,11,1401.6262,9.467879,0.0,1,4,premium,urban
|
||||
store_A,12,1180.2404,9.116606,0.0,0,5,premium,urban
|
||||
store_A,13,1230.1067,9.562768,0.0,0,6,premium,urban
|
||||
store_A,14,1350.9026,9.587188,1.0,0,0,premium,urban
|
||||
store_A,15,1122.653,10.323833,0.0,0,1,premium,urban
|
||||
store_A,16,1189.6578,10.114064,0.0,0,2,premium,urban
|
||||
store_A,17,1114.2455,10.567797,0.0,0,3,premium,urban
|
||||
store_A,18,1209.6483,10.328627,0.0,0,4,premium,urban
|
||||
store_A,19,1171.0994,9.812774,0.0,0,5,premium,urban
|
||||
store_A,20,1294.5083,10.62804,1.0,0,6,premium,urban
|
||||
store_A,21,1141.081,9.333946,0.0,0,0,premium,urban
|
||||
store_A,22,1236.6909,9.045424,0.0,0,1,premium,urban
|
||||
store_A,23,1359.1321,9.180096,0.0,1,2,premium,urban
|
||||
store_A,24,1113.6208,10.444718,0.0,0,3,premium,urban
|
||||
store_A,25,1120.9719,9.923755,0.0,0,4,premium,urban
|
||||
store_A,26,1170.1646,9.322543,0.0,0,5,premium,urban
|
||||
store_A,27,1141.1768,10.0020895,0.0,0,6,premium,urban
|
||||
store_A,28,1300.6125,9.304625,1.0,0,0,premium,urban
|
||||
store_A,29,1273.2278,10.392641,1.0,0,1,premium,urban
|
||||
store_A,30,1212.7638,9.892313,0.0,0,2,premium,urban
|
||||
store_A,31,1082.632,9.762042,0.0,0,3,premium,urban
|
||||
store_A,32,1076.0151,9.6030245,0.0,0,4,premium,urban
|
||||
store_A,33,1044.249,10.260565,0.0,0,5,premium,urban
|
||||
store_A,34,1124.0281,9.723625,0.0,0,6,premium,urban
|
||||
store_A,35,1359.397,9.1753,0.0,1,0,premium,urban
|
||||
store_A,36,1096.0808,9.2360115,0.0,0,1,premium,urban
|
||||
store_A,37,1027.4221,10.923796,0.0,0,2,premium,urban
|
||||
store_A,38,1033.1619,10.817162,0.0,0,3,premium,urban
|
||||
store_A,39,1269.5414,10.399414,1.0,0,4,premium,urban
|
||||
store_A,40,1147.2571,9.53174,0.0,0,5,premium,urban
|
||||
store_A,41,1116.2965,10.938353,0.0,0,6,premium,urban
|
||||
store_A,42,1072.0729,10.557502,0.0,0,0,premium,urban
|
||||
store_A,43,1129.3868,10.433781,0.0,0,1,premium,urban
|
||||
store_A,44,1295.5614,9.898723,1.0,0,2,premium,urban
|
||||
store_A,45,1320.1937,9.544483,1.0,0,3,premium,urban
|
||||
store_A,46,1223.4036,9.192781,0.0,0,4,premium,urban
|
||||
store_A,47,1523.2692,10.805204,1.0,1,5,premium,urban
|
||||
store_A,48,1229.2423,9.911552,0.0,0,6,premium,urban
|
||||
store_A,49,1224.824,9.404727,0.0,0,0,premium,urban
|
||||
store_A,50,1248.2861,9.611914,0.0,0,1,premium,urban
|
||||
store_A,51,1621.3419,10.158439,1.0,1,2,premium,urban
|
||||
store_A,52,1200.0713,9.353545,0.0,0,3,premium,urban
|
||||
store_A,53,1246.8055,10.713228,0.0,0,4,premium,urban
|
||||
store_A,54,1260.0721,10.517039,0.0,0,5,premium,urban
|
||||
store_A,55,1419.738,10.438926,1.0,0,6,premium,urban
|
||||
store_A,56,1465.4315,9.864186,1.0,0,0,premium,urban
|
||||
store_A,57,1411.4612,10.254618,0.0,0,1,premium,urban
|
||||
store_A,58,1459.6567,10.168196,1.0,0,2,premium,urban
|
||||
store_A,59,1562.2711,10.299693,1.0,0,3,premium,urban
|
||||
store_B,0,949.5817,10.130472,0.0,1,0,premium,urban
|
||||
store_B,1,826.9795,10.529998,0.0,0,1,premium,urban
|
||||
store_B,2,795.8978,10.269437,0.0,0,2,premium,urban
|
||||
store_B,3,781.1968,10.107159,0.0,0,3,premium,urban
|
||||
store_B,4,869.75146,10.118414,0.0,0,4,premium,urban
|
||||
store_B,5,840.91705,9.607901,0.0,0,5,premium,urban
|
||||
store_B,6,900.90045,9.061636,0.0,0,6,premium,urban
|
||||
store_B,7,862.10693,9.873435,0.0,0,0,premium,urban
|
||||
store_B,8,811.1614,9.42917,0.0,0,1,premium,urban
|
||||
store_B,9,814.42114,9.817058,1.0,0,2,premium,urban
|
||||
store_B,10,953.70746,10.706806,0.0,0,3,premium,urban
|
||||
store_B,11,1161.8647,9.467879,0.0,1,4,premium,urban
|
||||
store_B,12,901.0838,9.116606,0.0,0,5,premium,urban
|
||||
store_B,13,896.9283,9.562768,0.0,0,6,premium,urban
|
||||
store_B,14,1121.0658,9.587188,1.0,0,0,premium,urban
|
||||
store_B,15,1012.14496,10.323833,0.0,0,1,premium,urban
|
||||
store_B,16,845.7787,10.114064,0.0,0,2,premium,urban
|
||||
store_B,17,942.0486,10.567797,0.0,0,3,premium,urban
|
||||
store_B,18,894.31323,10.328627,0.0,0,4,premium,urban
|
||||
store_B,19,1029.0061,9.812774,0.0,0,5,premium,urban
|
||||
store_B,20,896.51886,10.62804,1.0,0,6,premium,urban
|
||||
store_B,21,1061.0464,9.333946,0.0,0,0,premium,urban
|
||||
store_B,22,963.2019,9.045424,0.0,0,1,premium,urban
|
||||
store_B,23,1091.6201,9.180096,0.0,1,2,premium,urban
|
||||
store_B,24,915.2826,10.444718,0.0,0,3,premium,urban
|
||||
store_B,25,771.0792,9.923755,0.0,0,4,premium,urban
|
||||
store_B,26,858.0784,9.322543,0.0,0,5,premium,urban
|
||||
store_B,27,814.89954,10.0020895,0.0,0,6,premium,urban
|
||||
store_B,28,916.48206,9.304625,1.0,0,0,premium,urban
|
||||
store_B,29,772.1533,10.392641,1.0,0,1,premium,urban
|
||||
store_B,30,803.5763,9.892313,0.0,0,2,premium,urban
|
||||
store_B,31,862.519,9.762042,0.0,0,3,premium,urban
|
||||
store_B,32,737.1871,9.6030245,0.0,0,4,premium,urban
|
||||
store_B,33,785.4303,10.260565,0.0,0,5,premium,urban
|
||||
store_B,34,906.9479,9.723625,0.0,0,6,premium,urban
|
||||
store_B,35,994.5817,9.1753,0.0,1,0,premium,urban
|
||||
store_B,36,1004.37634,9.2360115,0.0,0,1,premium,urban
|
||||
store_B,37,979.0918,10.923796,0.0,0,2,premium,urban
|
||||
store_B,38,870.12354,10.817162,0.0,0,3,premium,urban
|
||||
store_B,39,785.6754,10.399414,1.0,0,4,premium,urban
|
||||
store_B,40,769.2815,9.53174,0.0,0,5,premium,urban
|
||||
store_B,41,963.49274,10.938353,0.0,0,6,premium,urban
|
||||
store_B,42,831.17865,10.557502,0.0,0,0,premium,urban
|
||||
store_B,43,830.58295,10.433781,0.0,0,1,premium,urban
|
||||
store_B,44,794.41534,9.898723,1.0,0,2,premium,urban
|
||||
store_B,45,835.0851,9.544483,1.0,0,3,premium,urban
|
||||
store_B,46,885.5207,9.192781,0.0,0,4,premium,urban
|
||||
store_B,47,1178.3236,10.805204,1.0,1,5,premium,urban
|
||||
store_B,48,993.4054,9.911552,0.0,0,6,premium,urban
|
||||
store_B,49,841.88434,9.404727,0.0,0,0,premium,urban
|
||||
store_B,50,883.09314,9.611914,0.0,0,1,premium,urban
|
||||
store_B,51,1036.8414,10.158439,1.0,1,2,premium,urban
|
||||
store_B,52,903.3836,9.353545,0.0,0,3,premium,urban
|
||||
store_B,53,965.40485,10.713228,0.0,0,4,premium,urban
|
||||
store_B,54,1031.0249,10.517039,0.0,0,5,premium,urban
|
||||
store_B,55,1094.0964,10.438926,1.0,0,6,premium,urban
|
||||
store_B,56,988.38293,9.864186,1.0,0,0,premium,urban
|
||||
store_B,57,911.7493,10.254618,0.0,0,1,premium,urban
|
||||
store_B,58,1025.1101,10.168196,1.0,0,2,premium,urban
|
||||
store_B,59,978.6775,10.299693,1.0,0,3,premium,urban
|
||||
store_C,0,728.35284,10.130472,0.0,1,0,premium,urban
|
||||
store_C,1,503.7172,10.529998,0.0,0,1,premium,urban
|
||||
store_C,2,557.5812,10.269437,0.0,0,2,premium,urban
|
||||
store_C,3,579.2723,10.107159,0.0,0,3,premium,urban
|
||||
store_C,4,557.2319,10.118414,0.0,0,4,premium,urban
|
||||
store_C,5,573.1017,9.607901,0.0,0,5,premium,urban
|
||||
store_C,6,581.31024,9.061636,0.0,0,6,premium,urban
|
||||
store_C,7,567.57776,9.873435,0.0,0,0,premium,urban
|
||||
store_C,8,606.85065,9.42917,0.0,0,1,premium,urban
|
||||
store_C,9,618.42255,9.817058,1.0,0,2,premium,urban
|
||||
store_C,10,637.49005,10.706806,0.0,0,3,premium,urban
|
||||
store_C,11,864.7779,9.467879,0.0,1,4,premium,urban
|
||||
store_C,12,571.1436,9.116606,0.0,0,5,premium,urban
|
||||
store_C,13,612.2043,9.562768,0.0,0,6,premium,urban
|
||||
store_C,14,872.13513,9.587188,1.0,0,0,premium,urban
|
||||
store_C,15,738.0299,10.323833,0.0,0,1,premium,urban
|
||||
store_C,16,604.6675,10.114064,0.0,0,2,premium,urban
|
||||
store_C,17,650.33057,10.567797,0.0,0,3,premium,urban
|
||||
store_C,18,661.12146,10.328627,0.0,0,4,premium,urban
|
||||
store_C,19,603.7142,9.812774,0.0,0,5,premium,urban
|
||||
store_C,20,828.2985,10.62804,1.0,0,6,premium,urban
|
||||
store_C,21,669.9662,9.333946,0.0,0,0,premium,urban
|
||||
store_C,22,638.91095,9.045424,0.0,0,1,premium,urban
|
||||
store_C,23,838.9723,9.180096,0.0,1,2,premium,urban
|
||||
store_C,24,834.94836,10.444718,0.0,0,3,premium,urban
|
||||
store_C,25,555.9125,9.923755,0.0,0,4,premium,urban
|
||||
store_C,26,477.89877,9.322543,0.0,0,5,premium,urban
|
||||
store_C,27,651.99023,10.0020895,0.0,0,6,premium,urban
|
||||
store_C,28,535.84216,9.304625,1.0,0,0,premium,urban
|
||||
store_C,29,523.2324,10.392641,1.0,0,1,premium,urban
|
||||
store_C,30,595.6628,9.892313,0.0,0,2,premium,urban
|
||||
store_C,31,429.21732,9.762042,0.0,0,3,premium,urban
|
||||
store_C,32,595.64905,9.6030245,0.0,0,4,premium,urban
|
||||
store_C,33,574.6885,10.260565,0.0,0,5,premium,urban
|
||||
store_C,34,477.18958,9.723625,0.0,0,6,premium,urban
|
||||
store_C,35,703.0953,9.1753,0.0,1,0,premium,urban
|
||||
store_C,36,530.65405,9.2360115,0.0,0,1,premium,urban
|
||||
store_C,37,506.09885,10.923796,0.0,0,2,premium,urban
|
||||
store_C,38,417.0998,10.817162,0.0,0,3,premium,urban
|
||||
store_C,39,526.0255,10.399414,1.0,0,4,premium,urban
|
||||
store_C,40,635.823,9.53174,0.0,0,5,premium,urban
|
||||
store_C,41,495.87946,10.938353,0.0,0,6,premium,urban
|
||||
store_C,42,534.13354,10.557502,0.0,0,0,premium,urban
|
||||
store_C,43,557.8907,10.433781,0.0,0,1,premium,urban
|
||||
store_C,44,535.6469,9.898723,1.0,0,2,premium,urban
|
||||
store_C,45,590.8869,9.544483,1.0,0,3,premium,urban
|
||||
store_C,46,574.78455,9.192781,0.0,0,4,premium,urban
|
||||
store_C,47,796.0737,10.805204,1.0,1,5,premium,urban
|
||||
store_C,48,546.10583,9.911552,0.0,0,6,premium,urban
|
||||
store_C,49,580.9428,9.404727,0.0,0,0,premium,urban
|
||||
store_C,50,606.4677,9.611914,0.0,0,1,premium,urban
|
||||
store_C,51,851.0876,10.158439,1.0,1,2,premium,urban
|
||||
store_C,52,763.8405,9.353545,0.0,0,3,premium,urban
|
||||
store_C,53,824.2607,10.713228,0.0,0,4,premium,urban
|
||||
store_C,54,656.9345,10.517039,0.0,0,5,premium,urban
|
||||
store_C,55,813.55115,10.438926,1.0,0,6,premium,urban
|
||||
store_C,56,885.26666,9.864186,1.0,0,0,premium,urban
|
||||
store_C,57,618.21106,10.254618,0.0,0,1,premium,urban
|
||||
store_C,58,649.7526,10.168196,1.0,0,2,premium,urban
|
||||
store_C,59,649.2765,10.299693,1.0,0,3,premium,urban
|
||||
store_id,week,split,sales,price,promotion,holiday,day_of_week,store_type,region
|
||||
store_A,0,context,1372.64,11.6299,1,1,0,premium,urban
|
||||
store_A,1,context,965.54,11.9757,0,0,1,premium,urban
|
||||
store_A,2,context,1076.92,11.7269,0,0,2,premium,urban
|
||||
store_A,3,context,1094.09,12.1698,0,0,3,premium,urban
|
||||
store_A,4,context,970.18,11.9372,0,0,4,premium,urban
|
||||
store_A,5,context,1010.04,12.3327,0,0,5,premium,urban
|
||||
store_A,6,context,1098.7,12.2003,0,0,6,premium,urban
|
||||
store_A,7,context,1097.79,11.8124,0,0,0,premium,urban
|
||||
store_A,8,context,1114.81,12.3323,0,0,1,premium,urban
|
||||
store_A,9,context,1084.8,12.3048,0,0,2,premium,urban
|
||||
store_A,10,context,1339.72,11.8875,1,0,3,premium,urban
|
||||
store_A,11,context,1395.22,11.7883,0,1,4,premium,urban
|
||||
store_A,12,context,1158.92,12.1825,0,0,5,premium,urban
|
||||
store_A,13,context,1228.57,11.6398,0,0,6,premium,urban
|
||||
store_A,14,context,1198.65,11.6999,0,0,0,premium,urban
|
||||
store_A,15,context,1138.98,11.5074,0,0,1,premium,urban
|
||||
store_A,16,context,1186.2,12.2869,0,0,2,premium,urban
|
||||
store_A,17,context,1122.3,12.1649,0,0,3,premium,urban
|
||||
store_A,18,context,1212.12,12.2052,0,0,4,premium,urban
|
||||
store_A,19,context,1161.74,12.2807,0,0,5,premium,urban
|
||||
store_A,20,context,1157.89,11.9589,0,0,6,premium,urban
|
||||
store_A,21,context,1126.39,12.0687,0,0,0,premium,urban
|
||||
store_A,22,context,1224.8,11.6398,0,0,1,premium,urban
|
||||
store_A,23,context,1350.44,11.6145,0,1,2,premium,urban
|
||||
store_A,24,horizon,1119.15,12.1684,0,0,3,premium,urban
|
||||
store_A,25,horizon,1120.03,11.9711,0,0,4,premium,urban
|
||||
store_A,26,horizon,1155.31,12.0652,0,0,5,premium,urban
|
||||
store_A,27,horizon,1285.92,12.265,1,0,6,premium,urban
|
||||
store_A,28,horizon,1284.01,12.1347,1,0,0,premium,urban
|
||||
store_A,29,horizon,1130.01,12.0536,0,0,1,premium,urban
|
||||
store_A,30,horizon,1209.43,12.0592,0,0,2,premium,urban
|
||||
store_A,31,horizon,1231.79,11.804,1,0,3,premium,urban
|
||||
store_A,32,horizon,1077.46,11.5308,0,0,4,premium,urban
|
||||
store_A,33,horizon,1050.73,11.9367,0,0,5,premium,urban
|
||||
store_A,34,horizon,1124.21,11.7146,0,0,6,premium,urban
|
||||
store_A,35,horizon,1344.73,11.9085,0,1,0,premium,urban
|
||||
store_B,0,context,1053.03,9.9735,1,1,0,standard,suburban
|
||||
store_B,1,context,903.51,9.767,1,0,1,standard,suburban
|
||||
store_B,2,context,826.82,9.8316,0,0,2,standard,suburban
|
||||
store_B,3,context,709.93,10.0207,0,0,3,standard,suburban
|
||||
store_B,4,context,834.42,9.9389,0,0,4,standard,suburban
|
||||
store_B,5,context,847.01,9.5216,0,0,5,standard,suburban
|
||||
store_B,6,context,802.58,10.3263,0,0,6,standard,suburban
|
||||
store_B,7,context,770.87,10.3962,0,0,0,standard,suburban
|
||||
store_B,8,context,873.1,9.6402,0,0,1,standard,suburban
|
||||
store_B,9,context,844.74,10.054,0,0,2,standard,suburban
|
||||
store_B,10,context,1050.46,9.6086,1,0,3,standard,suburban
|
||||
store_B,11,context,1085.99,10.1722,0,1,4,standard,suburban
|
||||
store_B,12,context,978.74,9.7812,0,0,5,standard,suburban
|
||||
store_B,13,context,1033.59,10.1594,1,0,6,standard,suburban
|
||||
store_B,14,context,846.06,10.227,0,0,0,standard,suburban
|
||||
store_B,15,context,906.93,10.2686,0,0,1,standard,suburban
|
||||
store_B,16,context,922.35,9.6077,0,0,2,standard,suburban
|
||||
store_B,17,context,1111.93,10.416,1,0,3,standard,suburban
|
||||
store_B,18,context,946.95,9.7302,0,0,4,standard,suburban
|
||||
store_B,19,context,923.2,9.5374,0,0,5,standard,suburban
|
||||
store_B,20,context,963.38,10.0549,0,0,6,standard,suburban
|
||||
store_B,21,context,978.7,9.8709,1,0,0,standard,suburban
|
||||
store_B,22,context,840.39,10.3298,0,0,1,standard,suburban
|
||||
store_B,23,context,1019.22,10.3083,0,1,2,standard,suburban
|
||||
store_B,24,horizon,848.1,9.8171,0,0,3,standard,suburban
|
||||
store_B,25,horizon,777.91,10.4529,0,0,4,standard,suburban
|
||||
store_B,26,horizon,883.44,9.7909,0,0,5,standard,suburban
|
||||
store_B,27,horizon,827.78,10.0151,0,0,6,standard,suburban
|
||||
store_B,28,horizon,762.41,9.756,0,0,0,standard,suburban
|
||||
store_B,29,horizon,763.79,10.436,0,0,1,standard,suburban
|
||||
store_B,30,horizon,838.41,9.6646,0,0,2,standard,suburban
|
||||
store_B,31,horizon,860.45,9.5449,0,0,3,standard,suburban
|
||||
store_B,32,horizon,904.82,9.9351,0,0,4,standard,suburban
|
||||
store_B,33,horizon,1084.74,10.4924,1,0,5,standard,suburban
|
||||
store_B,34,horizon,808.09,10.3917,0,0,6,standard,suburban
|
||||
store_B,35,horizon,938.26,10.2486,0,1,0,standard,suburban
|
||||
store_C,0,context,709.43,7.1053,0,1,0,discount,rural
|
||||
store_C,1,context,649.01,7.0666,1,0,1,discount,rural
|
||||
store_C,2,context,660.66,7.5944,1,0,2,discount,rural
|
||||
store_C,3,context,750.17,7.1462,1,0,3,discount,rural
|
||||
store_C,4,context,726.88,7.8247,1,0,4,discount,rural
|
||||
store_C,5,context,639.97,7.3103,0,0,5,discount,rural
|
||||
store_C,6,context,580.71,7.1439,0,0,6,discount,rural
|
||||
store_C,7,context,549.13,7.921,0,0,0,discount,rural
|
||||
store_C,8,context,597.79,7.1655,0,0,1,discount,rural
|
||||
store_C,9,context,627.48,7.2847,0,0,2,discount,rural
|
||||
store_C,10,context,634.26,7.1536,0,0,3,discount,rural
|
||||
store_C,11,context,928.07,7.1155,1,1,4,discount,rural
|
||||
store_C,12,context,643.37,7.0211,0,0,5,discount,rural
|
||||
store_C,13,context,652.8,7.0554,0,0,6,discount,rural
|
||||
store_C,14,context,766.65,7.1746,0,0,0,discount,rural
|
||||
store_C,15,context,737.37,7.0534,0,0,1,discount,rural
|
||||
store_C,16,context,589.02,7.5911,0,0,2,discount,rural
|
||||
store_C,17,context,613.06,7.6807,0,0,3,discount,rural
|
||||
store_C,18,context,556.25,7.3936,0,0,4,discount,rural
|
||||
store_C,19,context,596.46,7.318,0,0,5,discount,rural
|
||||
store_C,20,context,632.0,7.5045,0,0,6,discount,rural
|
||||
store_C,21,context,662.1,7.875,0,0,0,discount,rural
|
||||
store_C,22,context,558.0,7.8511,0,0,1,discount,rural
|
||||
store_C,23,context,769.38,7.0435,0,1,2,discount,rural
|
||||
store_C,24,horizon,482.94,7.1815,0,0,3,discount,rural
|
||||
store_C,25,horizon,571.69,7.2367,0,0,4,discount,rural
|
||||
store_C,26,horizon,666.89,7.2494,1,0,5,discount,rural
|
||||
store_C,27,horizon,677.55,7.5712,1,0,6,discount,rural
|
||||
store_C,28,horizon,503.9,7.4163,0,0,0,discount,rural
|
||||
store_C,29,horizon,541.34,7.0493,0,0,1,discount,rural
|
||||
store_C,30,horizon,443.17,7.3736,0,0,2,discount,rural
|
||||
store_C,31,horizon,596.87,7.5238,1,0,3,discount,rural
|
||||
store_C,32,horizon,628.12,7.1017,0,0,4,discount,rural
|
||||
store_C,33,horizon,586.61,7.8335,1,0,5,discount,rural
|
||||
store_C,34,horizon,456.82,7.052,0,0,6,discount,rural
|
||||
store_C,35,horizon,782.3,7.9248,0,1,0,discount,rural
|
||||
|
||||
|
Reference in New Issue
Block a user