M5 Department Sales Forecasting

Forecast retail department-level sales based on the M5 forecasting competition data. 7 departments tracked over 1,941 time periods.

Dataset Exploration

The M5 Department dataset is based on the M5 forecasting competition and contains Walmart department-level sales data. Features include CPI, holiday events (sporting, cultural, national, religious), SNAP benefits by state, day of week, and month.

Panel data structure: 7 department aggregations tracked over 1,941 days (13,587 total rows, 13 columns). Each department (e.g. FOODS_1) is tracked daily.

Fit Data (first 5 rows)

agg_id	ds	CPI	snap_CA	snap_TX	snap_WI	wday	month	Sales
FOODS_1	40572	1	0	0	0	1	1	2343
FOODS_1	40573	1.04	0	0	0	2	1	2216
FOODS_1	40574	1.053	0	0	0	3	1	1657
FOODS_1	40575	1.066	1	1	0	4	2	1508
FOODS_1	40576	1.075	1	0	1	5	2	1209

Forecast Data (first 3 rows)

agg_id	ds	CPI	wday	month	Sales
FOODS_1	42513	1.201	3	5	3246
FOODS_1	42514	1.201	4	5	3270
FOODS_1	42515	1.201	5	5	3274

Download sample datasets from GitHub

Code Walkthrough

Step 1: Initialize

Python

import pandas as pd
from datfid import DATFIDClient

client = DATFIDClient(token="your_DATFID_token")

Step 2: Fit the Model

Python

url_fit = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/M5_Department.xlsx"
df = pd.read_excel(url_fit)

result = client.fit_model(
    df=df,
    id_col="agg_id",
    time_col="ds",
    y="Sales",
    current_features="all",
    filter_by_significance=True
)

Step 3: Forecast

Python

url_forecast = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/M5_Department_forecast.xlsx"
df_forecast = pd.read_excel(url_forecast)

forecast = client.forecast_model(df_forecast=df_forecast)

Analysis Results (Model Fit)

Formula

Sales ~ α1*Intercept + β1*CPI + β2*Sporting + β3*Cultural + β4*National + β5*Religious + β6*snap_CA + β7*snap_TX + β8*snap_WI + β9*wday + β10*month

Alpha Estimates (Time-Invariant)

Variable	Estimate	T-stat	Interpretation
Intercept	-24,015	518.46	Baseline department sales level

Beta Estimates (Time-Varying)

Variable	Estimate	T-stat	Interpretation
CPI	+25,321.8	45.18	A 1-unit CPI increase drives sales up by ~25,322 units (inflation-linked pricing)
wday	-242.33	38.64	Each additional weekday reduces sales by ~242 units (weekends sell more)
National	-758.56	9.68	National holidays reduce sales by ~759 units (stores may close or reduce hours)
snap_TX	+256.73	7.79	SNAP benefits in Texas increase sales by ~257 units
snap_WI	+242.74	7.36	SNAP benefits in Wisconsin increase sales by ~243 units
snap_CA	+212.01	7.07	SNAP benefits in California increase sales by ~212 units
month	-16.39	4.47	Each later month reduces sales by ~16 units
Cultural	-205.29	2.31	Cultural holidays reduce sales by ~205 units (p=0.021)
Religious	-97.62	1.29	Not statistically significant (p=0.197)
Sporting	+51.14	0.37	Not statistically significant (p=0.713)

Model Performance

0.021

R² Overall

≈0

R² Between

0.230

R² Within

3,821

MAE

2.91e7

MSE

The M5 dataset has high variance across departments (FOODS vs HOBBIES vs HOUSEHOLD), which explains the lower between-group R². Adding lagged features or filtering by significance can improve results.

Try it yourself: Select "M5 Department" in the Free Playground.