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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
165 lines
6.3 KiB
Markdown
165 lines
6.3 KiB
Markdown
# MdAPE: Median Absolute Percentage Error
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> *When you need relative errors but can't trust the outliers.*
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| Property | Value |
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| ---------------- | -------------------------------- |
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| **Category** | Error Metric |
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| **Inputs** | Actual, Predicted (dual series) |
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| **Parameters** | `period` |
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| **Outputs** | Single series (Mdape) |
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| **Output range** | $\geq 0$ |
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| **Warmup** | `period` bars |
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| **PineScript** | [mdape.pine](mdape.pine) |
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- Median Absolute Percentage Error (MdAPE) combines the scale-independence of percentage errors with the robustness of median statistics.
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- **Similar:** [MAPE](../mape/Mape.md), [MdAE](../mdae/Mdae.md) | **Trading note:** Median Absolute Percentage Error; robust version of MAPE for skewed error distributions.
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- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
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Median Absolute Percentage Error (MdAPE) combines the scale-independence of percentage errors with the robustness of median statistics. It provides a measure of typical relative prediction accuracy that remains stable even when some predictions are dramatically wrong.
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## Historical Context
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MdAPE arose as a natural combination of two statistical improvements: using percentages for scale-independence (like MAPE) and using medians for robustness (like MdAE). This hybrid approach addresses both the scale problem of MAE and the outlier sensitivity of MAPE.
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## Architecture & Physics
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MdAPE first normalizes each error as a percentage of the actual value, then finds the median of these percentages. This two-stage approach provides both relative context and outlier resistance.
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### Properties
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* **Scale-independent**: Comparable across different data magnitudes
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* **Outlier-robust**: Extreme errors don't skew results
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* **Percentage-based**: Results are interpretable as "typical % error"
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* **Non-negative**: MdAPE ≥ 0, with 0 indicating perfect prediction
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## Mathematical Foundation
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### 1. Absolute Percentage Error
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For each observation, calculate the percentage error:
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$$e_i = \frac{|y_i - \hat{y}_i|}{|y_i|} \times 100$$
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Where:
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* $y_i$ = actual value
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* $\hat{y}_i$ = predicted value
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### 2. Median Calculation
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Find the middle value of the sorted percentage errors:
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$$MdAPE = \text{median}(e_1, e_2, ..., e_n)$$
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### 3. Running Update (O(1))
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QuanTAlib uses a sorted ring buffer for efficient median retrieval:
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$$MdAPE = \begin{cases}
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e_{(n+1)/2} & \text{if } n \text{ is odd} \\
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\frac{e_{n/2} + e_{n/2+1}}{2} & \text{if } n \text{ is even}
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\end{cases}$$
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## Implementation Details
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### Usage Patterns
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```csharp
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// Streaming mode - update with each new observation
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var mdape = new Mdape(period: 20);
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var result = mdape.Update(actualValue, predictedValue);
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// Batch mode - calculate for entire series
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var results = Mdape.Calculate(actualSeries, predictedSeries, period: 20);
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// Span mode - zero-allocation for high performance
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Mdape.Batch(actualSpan, predictedSpan, outputSpan, period: 20);
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```
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### Parameters
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| Parameter | Type | Description |
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| :--- | :--- | :--- |
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| **period** | int | Lookback window for median calculation (must be > 0) |
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### Properties
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| Property | Type | Description |
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| :--- | :--- | :--- |
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| **Last** | TValue | Most recent MdAPE value (in percentage) |
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| **IsHot** | bool | True when buffer is full |
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| **Name** | string | Indicator name (e.g., "Mdape(20)") |
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| **WarmupPeriod** | int | Number of periods before valid output |
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## Performance Profile
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### Operation Count (Streaming Mode)
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O(1) per bar. Single-pass scalar transformation of (actual, forecast) pair; no lookback window required.
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| Operation | Count | Cost (cycles) | Subtotal |
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| :--- | :---: | :---: | :---: |
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| Error computation (subtract, abs/square/log) | 1-3 | ~3-8 cy | ~5-15 cy |
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| Running accumulator update (EMA or sum) | 1 | ~4 cy | ~4 cy |
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| **Total** | **2-4** | — | **~9-19 cycles** |
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Streaming update requires only the current actual/forecast pair and running state. ~10-15 cycles/bar typical.
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### Batch Mode (SIMD Analysis)
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| Operation | Vectorizable? | Notes |
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| :--- | :---: | :--- |
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| Element-wise error computation | Yes | Independent per bar; fully vectorizable with `Vector<double>` |
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| Reduction (sum/mean) | Yes | Parallel reduction; AVX2 gives 4x speedup |
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| Log/exp components | Partial | Transcendental ops; polynomial approx for SIMD |
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Batch SIMD: 4x-8x speedup for large windows. ~3-5 cy/bar amortized in vectorized batch mode.
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| Metric | Score | Notes |
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| :--- | :--- | :--- |
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| **Throughput** | ~25 ns/bar | O(1) with sorted buffer |
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| **Allocations** | 0 | Uses pre-allocated buffers |
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| **Complexity** | O(1) | Constant time per update |
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| **Accuracy** | 10/10 | Exact calculation |
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| **Timeliness** | 9/10 | No lag beyond the period |
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| **Robustness** | 10/10 | Immune to outliers |
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## Interpretation
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| MdAPE Range | Interpretation |
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| :--- | :--- |
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| **0%** | Perfect prediction |
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| **0-5%** | Excellent accuracy |
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| **5-10%** | Good accuracy |
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| **10-20%** | Acceptable accuracy |
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| **> 20%** | Poor accuracy |
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## Comparison with MAPE
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| Scenario | MAPE | MdAPE |
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| :--- | :--- | :--- |
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| **Normal distribution** | Similar values | Similar values |
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| **Single 1000% error** | Heavily inflated | Unchanged |
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| **Asymmetric errors** | Biased | Representative |
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| **Zero actual values** | Undefined | Undefined (uses substitution) |
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## Common Use Cases
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1. **Retail Forecasting**: Track typical accuracy across SKUs with varying prices
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2. **Financial Analysis**: Evaluate prediction quality ignoring market crashes
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3. **Model Selection**: Choose models based on typical rather than average performance
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4. **Operations Research**: Measure forecast reliability for planning
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## Edge Cases
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* **Zero Actual Values**: Substitutes with small epsilon to avoid division by zero
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* **NaN Handling**: Uses last valid value substitution
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* **Single Input**: Not supported (requires two series)
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* **Period = 1**: Returns current absolute percentage error
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* **All Perfect**: Returns 0%
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## Related Indicators
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* [MAPE](../mape/Mape.md) - Mean Absolute Percentage Error (uses mean)
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* [MdAE](../mdae/Mdae.md) - Median Absolute Error (non-percentage)
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* [SMAPE](../smape/Smape.md) - Symmetric MAPE (different normalization) |