Seasonal Trend Prediction
“What will total revenue be for each product category over the next quarter?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
What will total revenue be for each product category over the next quarter?
Seasonal planning based on year-over-year comparisons misses emerging trends, promotional lifts, and cross-category cannibalization. A 10% forecast error at the category level can mean $10–50M in misallocated inventory and marketing spend across a large retailer. When electronics surge because of a product launch while home goods soften, the YoY model sees neither shift until it is too late.
How KumoRFM solves this
Relational intelligence for every forecast
Kumo learns from the relational graph connecting categories to sales, products, promotions, and external signals. Instead of treating each category as an isolated time series, Kumo sees that the Electronics category's Q4 surge is amplified by an overlapping holiday promotion, that Home Office is cannibalizing Furniture, and that a new product launch in Wearables is pulling share from Accessories. These cross-category and cross-signal dependencies produce quarterly forecasts that capture the full picture.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
CATEGORIES
| category_id | category_name | department |
|---|---|---|
| CAT-10 | Electronics | Technology |
| CAT-20 | Home Office | Furniture |
| CAT-30 | Wearables | Accessories |
SALES
| sale_id | product_id | category_id | revenue | units | timestamp |
|---|---|---|---|---|---|
| SL-7001 | PRD-401 | CAT-10 | $249.99 | 1 | 2025-09-14 |
| SL-7002 | PRD-502 | CAT-20 | $189.00 | 1 | 2025-09-14 |
| SL-7003 | PRD-610 | CAT-30 | $89.95 | 2 | 2025-09-15 |
PROMOTIONS
| promo_id | category_id | discount_pct | start_date | end_date |
|---|---|---|---|---|
| PRM-01 | CAT-10 | 15 | 2025-11-20 | 2025-12-01 |
| PRM-02 | CAT-20 | 10 | 2025-10-01 | 2025-10-15 |
| PRM-03 | CAT-30 | 20 | 2025-11-25 | 2025-12-02 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(SALES.REVENUE, 0, 90, days) FOR EACH CATEGORIES.CATEGORY_ID
Prediction output
Every entity gets a score, updated continuously
| CATEGORY_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| CAT-10 | 2025-10-01 | $4.2M |
| CAT-20 | 2025-10-01 | $1.8M |
| CAT-30 | 2025-10-01 | $890K |
Understand why
Every prediction includes feature attributions — no black boxes
Category CAT-10 (Electronics)
Predicted: $4.2M revenue in next quarter
Top contributing features
Prior year same quarter
$3.6M
28% attribution
Promotional calendar overlap
2 promos
24% attribution
Cross-category trend (share gain)
+3.2%
20% attribution
Macro consumer sentiment
Positive
16% attribution
New product launches
4 SKUs
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Capture emerging trends and promotional lifts that YoY comparisons miss — reduce category-level forecast error by 30–40%.
Related use cases
Explore more forecasting use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
