Forecast Comparison

The Forecast Comparison view lets you compare the outputs of different forecast models side by side. Each model uses one of Tether’s 11 forecast algorithms, and comparing their outputs helps you choose the best model for your products.

Accessing Forecast Comparison

You can compare models in two ways:

Comparison page — Navigate to Demand Forecast → Forecast Comparison.
Inline comparison — Click any row in the forecast table to open a modal showing all model outputs for that SKU-channel combination.

Model comparison and selection is available to Admin users. Non-admin users can view the currently active model’s output on the forecast dashboard.

Why compare forecasts?

Comparing forecasts helps you:

Purpose	Benefit
Model selection	Choose the best-performing algorithm for your product mix
Accuracy analysis	Understand how well each model predicts actual sales
Segment optimization	Find which algorithms work best for specific product types
Ongoing improvement	Track forecast accuracy over time and switch models when needed

Comparison view

Layout

The comparison view shows:

Model list — all defined models with their algorithm type
Side-by-side outputs — forecast values from each model for the same SKU, channel, and period
Actuals — historical sales values for completed periods (visually distinguished from forecasts)
Variance — differences between each model’s forecast and actual sales

Available algorithms

Each model in the comparison uses one of the following algorithms. See Forecast Algorithms for full details.

Algorithm	Best for
90-Day Rolling Average	Stable, predictable demand
120-Day Rolling Average (Recursive)	Volatile products, strategic planning
Exponentially Weighted Moving Average (EWMA)	Trending products, recent pattern changes
Linear Trend	Products with consistent growth or decline
Simplified Seasonal	Seasonal and holiday-driven products
Simple Seasonal with Trend Adjustment	Seasonal products with growth trends
Seasonal Year-over-Year Growth	Seasonal products with observed annual growth
Rolling Momentum	Accelerating or decelerating products
Holt-Winters (Triple Exponential Smoothing)	Complex seasonal patterns with trends
SARIMA	Complex time series with seasonal components
Static Baseline	External or manually uploaded forecasts

Comparing models

Selecting models to compare

Open the comparison view

Navigate to Demand Forecast → Forecast Comparison, or click a row in the forecast table to open the inline model selection dialog.

View model outputs

The view shows each defined model as a row, with columns representing time periods. The currently active model is highlighted.

Compare values

Review forecast values side by side. For completed periods, all models show the actual sales value. For current and future periods, each model shows its own forecast output.

Select a model

If you want to switch, select a different model and click Apply to make it the active forecast.

Side-by-side view

The table shows forecast outputs from each model for the same SKU-channel combination:

SKU	Period	90-Day Rolling Avg	EWMA	Holt-Winters	Actual	Variance (Rolling Avg)	Variance (EWMA)
SKU-001	Jan	100	110	108	105	-5	+5
SKU-001	Feb	120	115	117	118	+2	-3
SKU-002	Jan	50	55	52	48	+2	+7

Completed periods display actual sales history, visually distinguished from forecast values. All models show the same actual value for completed periods.

Accuracy metrics

Key metrics

Tether calculates four accuracy metrics for each model’s forecast against actual sales:

Metric	Description	Formula	Good value
MAPE	Mean Absolute Percentage Error — average percentage difference between forecast and actual	`mean(\|actual - forecast\| / actual) × 100`	Lower is better (< 20%)
MAE	Mean Absolute Error — average absolute difference in units	`mean(\|actual - forecast\|)`	Lower is better
Bias	Systematic over- or under-forecasting tendency	`mean(forecast - actual) / mean(actual) × 100`	Close to 0%
Hit Rate	Percentage of periods where forecast falls within an acceptable range of actual	`% of periods where \|error\| < threshold`	Higher is better (> 75%)

Accuracy metrics are only calculated for completed periods where actual sales data is available. Future periods do not contribute to accuracy scores.

Understanding MAPE

MAPE (Mean Absolute Percentage Error) is the primary metric used for parameter optimization and model comparison:

MAPE = Average of |Actual - Forecast| / Actual × 100%

MAPE range	Interpretation
< 10%	Excellent accuracy
10–20%	Good accuracy
20–30%	Acceptable
> 30%	Needs improvement

MAPE can be misleading for SKUs with very low sales volumes — a difference of 1 unit on a product that sells 2 units shows 50% MAPE. Use MAE alongside MAPE for low-volume items.

Metrics cards

Summary cards show overall performance per model. For example:

90-Day Rolling Average
├── MAPE: 18.4%
├── MAE: 23 units
├── Bias: +2.1%
└── Hit Rate: 72%

Holt-Winters
├── MAPE: 12.8%
├── MAE: 19 units
├── Bias: -1.5%
└── Hit Rate: 82%

EWMA
├── MAPE: 15.1%
├── MAE: 21 units
├── Bias: +0.8%
└── Hit Rate: 78%

Filtering comparisons

By product

Compare model performance for specific products:

Filter by collection, tag, or individual SKU
See how each algorithm performs for different product types
Identify algorithms that excel for specific demand patterns

By time period

Analyze accuracy over different periods:

Period type	Insight
Recent (last 30–90 days)	Current model performance
Historical (6+ months)	Long-term accuracy trends
Seasonal (peak periods)	Performance during seasonal demand spikes
Post-event (after promotions)	Accuracy around demand disruptions

By channel

Compare accuracy across sales channels:

Some algorithms may perform better for specific channels (e.g., wholesale vs. DTC)
Channel-specific demand patterns affect algorithm suitability
Consider creating separate models for channels with very different demand profiles

Variance analysis

Understanding variance

Variance	Meaning
Positive	Forecast was higher than actual (over-forecast)
Negative	Forecast was lower than actual (under-forecast)
Near zero	Accurate forecast

Variance patterns

Look for systematic patterns in variance to diagnose model fit:

Pattern	Indicates	Action
Consistently positive	Model over-forecasts	Try a more reactive algorithm (e.g., EWMA) or reduce trend parameters
Consistently negative	Model under-forecasts	Check if growth trends are being captured; try Linear Trend or Seasonal YoY Growth
Random	No bias, normal variance	Model is well-calibrated
Seasonal misalignment	Seasonal pattern timing is off	Switch to a seasonal algorithm (Simplified Seasonal, Holt-Winters) or verify seasonal period settings

Model performance by segment

Product segments

Different algorithms tend to perform best for different demand patterns:

Segment	Recommended algorithms	Why
High volume, stable	90-Day Rolling Average, EWMA	Simple averages smooth noise on stable products
Seasonal products	Simplified Seasonal, Holt-Winters, SARIMA	Capture repeating seasonal patterns
Products with growth trends	Linear Trend, Seasonal YoY Growth	Model upward or downward demand trajectories
New or volatile products	EWMA, Rolling Momentum	React quickly to recent data without requiring long history
Complex seasonal + trend	Holt-Winters, SARIMA	Handle both trend and seasonality simultaneously
Externally planned	Static Baseline	Use your own uploaded forecast values

See Forecast Algorithms for detailed descriptions and parameter information for each algorithm.

Analyzing by segment

Filter to a product segment (by collection, tag, or channel)
Compare model metrics across the filtered set
Identify which algorithm performs best for that segment
Consider creating a model specifically configured for that segment

Best model selection

Choosing the right algorithm

Consider these factors when selecting an algorithm for a model:

Overall accuracy — Which has the lowest MAPE across your product set?
Bias — Is there systematic over- or under-forecasting?
Stability — Is accuracy consistent across periods, or does it vary widely?
Data requirements — Does the algorithm need more history than you have? (e.g., SARIMA and Holt-Winters need 2+ years of data for reliable seasonality)
Product fit — Does your demand pattern match what the algorithm models?

Algorithm selection guide

If your products have…	Consider…
Stable, predictable demand	90-Day Rolling Average, 120-Day Rolling Average
Recent trend changes	EWMA, Rolling Momentum
Clear seasonality	Simplified Seasonal, Holt-Winters
Seasonal patterns + growth	Seasonal YoY Growth, Simple Seasonal with Trend Adjustment
Complex seasonal patterns	SARIMA, Holt-Winters
Linear growth or decline	Linear Trend
External or manual forecasts	Static Baseline

Using comparison results

Switching models

If comparison reveals a better-performing model:

Note which algorithm performs best and for which products
Go to Forecast Admin → the Models tab
Either update the active model or create a new model with the better algorithm
Monitor accuracy after the switch to confirm improvement

Per-SKU optimization

Tether supports different models for different SKU-channel combinations:

Create multiple models, each with a different algorithm
Assign the best-performing model per SKU-channel pair — see Model assignment
Use comparison metrics to validate your choices over time

Start with a broadly accurate algorithm (like EWMA or Holt-Winters), then create specialized models for product segments where a different algorithm significantly outperforms.

Reporting

Exporting comparison data

Set up your view

Configure the models, filters, and date range you want to export.

Export

Click the Export button to download comparison data.

Review

The export includes forecast values, actuals, variances, and accuracy metrics for all selected models.

Using comparison reports

Exports are useful for:

Monthly accuracy reviews with stakeholders
Documenting model selection decisions
Identifying trends in forecast performance over time

Best practices

Compare regularly

Review model performance periodically:

Monthly for businesses with frequent demand changes
Quarterly for stable, predictable businesses
After major events — promotions, product launches, or supply disruptions

Use enough data

Don’t judge algorithms on too little history:

Minimum 3–6 months of actuals for meaningful comparison
Include at least one full seasonal cycle for seasonal algorithms
Account for unusual events (stockouts, promotions) that may skew metrics

Consider business context

Pure accuracy isn’t the only factor:

Under-forecasting may cause stockouts and lost sales
Over-forecasting ties up capital in excess inventory
Check the Bias metric to see if a model consistently over- or under-forecasts
Choose the error direction that’s less costly for your business

Document model changes

When switching models:

Record which algorithm was replaced and why
Note the accuracy improvement you expect
Set a follow-up date to validate the change worked

Next steps

Forecast Algorithms

Learn how each algorithm works and when to use it

Forecast Admin

Configure and manage forecast models

Forecast Dashboard

View and edit forecasts

Sales History

Analyze historical data

​Forecast Comparison

​Accessing Forecast Comparison

​Why compare forecasts?

​Comparison view

​Layout

​Available algorithms

​Comparing models

​Selecting models to compare

​Side-by-side view

​Accuracy metrics

​Key metrics

​Understanding MAPE

​Metrics cards

​Filtering comparisons

​By product

​By time period

​By channel

​Variance analysis

​Understanding variance

​Variance patterns

​Model performance by segment

​Product segments

​Analyzing by segment

​Best model selection

​Choosing the right algorithm

​Algorithm selection guide

​Using comparison results

​Switching models

​Per-SKU optimization

​Reporting

​Exporting comparison data

​Using comparison reports

​Best practices

​Next steps

Forecast Algorithms

Forecast Admin

Forecast Dashboard

Sales History

Forecast Comparison

Accessing Forecast Comparison

Why compare forecasts?

Comparison view

Layout

Available algorithms

Comparing models

Selecting models to compare

Side-by-side view

Accuracy metrics

Key metrics

Understanding MAPE

Metrics cards

Filtering comparisons

By product

By time period

By channel

Variance analysis

Understanding variance

Variance patterns

Model performance by segment

Product segments

Analyzing by segment

Best model selection

Choosing the right algorithm

Algorithm selection guide

Using comparison results

Switching models

Per-SKU optimization

Reporting

Exporting comparison data

Using comparison reports

Best practices

Next steps