.forecast-accuracy-calculator-wrapper {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
max-width: 850px;
margin: 20px auto;
padding: 25px;
border: 1px solid #e0e0e0;
border-radius: 10px;
background-color: #ffffff;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);
color: #333;
}
.forecast-accuracy-calculator-wrapper h2 {
color: #2c3e50;
text-align: center;
margin-bottom: 25px;
font-size: 2em;
border-bottom: 2px solid #3498db;
padding-bottom: 10px;
}
.forecast-accuracy-calculator-wrapper h3 {
color: #34495e;
margin-top: 25px;
margin-bottom: 15px;
font-size: 1.5em;
}
.forecast-accuracy-calculator-wrapper p {
line-height: 1.7;
margin-bottom: 12px;
font-size: 1em;
}
.forecast-accuracy-calculator-wrapper ul {
list-style-type: disc;
margin-left: 20px;
margin-bottom: 15px;
}
.forecast-accuracy-calculator-wrapper ul li {
margin-bottom: 8px;
line-height: 1.6;
}
.forecast-accuracy-calculator-wrapper .calculator-section {
background-color: #f8f9fa;
border: 1px solid #e9ecef;
border-radius: 8px;
padding: 20px;
margin-top: 30px;
}
.forecast-accuracy-calculator-wrapper .forecast-pair {
display: flex;
flex-wrap: wrap;
align-items: center;
margin-bottom: 15px;
padding: 12px;
border: 1px solid #dee2e6;
border-radius: 6px;
background-color: #fff;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
}
.forecast-accuracy-calculator-wrapper .forecast-pair label {
flex: 1 1 180px;
margin-right: 15px;
font-weight: 600;
color: #555;
font-size: 0.95em;
}
.forecast-accuracy-calculator-wrapper .forecast-pair input[type="number"] {
flex: 1 1 120px;
padding: 10px;
border: 1px solid #ced4da;
border-radius: 5px;
margin-right: 15px;
box-sizing: border-box;
font-size: 1em;
}
.forecast-accuracy-calculator-wrapper .forecast-pair button {
padding: 8px 14px;
background-color: #dc3545;
color: white;
border: none;
border-radius: 5px;
cursor: pointer;
font-size: 0.9em;
transition: background-color 0.2s ease;
}
.forecast-accuracy-calculator-wrapper .forecast-pair button:hover {
background-color: #c82333;
}
.forecast-accuracy-calculator-wrapper .action-buttons {
text-align: center;
margin-top: 25px;
}
.forecast-accuracy-calculator-wrapper .action-buttons button {
display: inline-block;
padding: 12px 20px;
margin: 0 10px;
background-color: #3498db;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-size: 1.05em;
font-weight: 600;
transition: background-color 0.2s ease, transform 0.2s ease;
}
.forecast-accuracy-calculator-wrapper .action-buttons button:hover {
background-color: #2980b9;
transform: translateY(-2px);
}
.forecast-accuracy-calculator-wrapper #accuracyResult {
margin-top: 30px;
padding: 20px;
border: 1px solid #d4edda;
border-radius: 8px;
background-color: #e6ffed;
color: #155724;
font-size: 1.05em;
line-height: 1.8;
}
.forecast-accuracy-calculator-wrapper #accuracyResult h3 {
color: #155724;
margin-top: 0;
font-size: 1.6em;
}
.forecast-accuracy-calculator-wrapper #accuracyResult p {
margin-bottom: 8px;
}
.forecast-accuracy-calculator-wrapper .note {
font-size: 0.9em;
color: #6c757d;
margin-top: 10px;
font-style: italic;
}
var pairCounter = 0; // Global counter for unique IDs
// Function to add a new forecast pair input row
function addForecastPair(actualVal, forecastVal) {
pairCounter++;
var container = document.getElementById('forecastPairsContainer');
var newPairDiv = document.createElement('div');
newPairDiv.className = 'forecast-pair';
newPairDiv.id = 'pair_' + pairCounter;
newPairDiv.innerHTML = `
`;
container.appendChild(newPairDiv);
}
// Function to remove a forecast pair input row
function removeForecastPair(buttonElement) {
buttonElement.parentNode.remove();
}
// Initialize with 3 example pairs when the page loads
window.onload = function() {
addForecastPair(100, 95);
addForecastPair(110, 115);
addForecastPair(120, 118);
};
// Main calculation function
function calculateAccuracy() {
var actualInputs = document.querySelectorAll('.actual-value-input');
var forecastInputs = document.querySelectorAll('.forecast-value-input');
var resultDiv = document.getElementById('accuracyResult');
var totalAbsoluteError = 0;
var totalSquaredError = 0;
var totalPercentageError = 0;
var totalError = 0;
var validPeriods = 0;
var mapeValidPeriods = 0;
if (actualInputs.length === 0) {
resultDiv.innerHTML = "Please add at least one period of data using the 'Add More Periods' button.";
return;
}
for (var i = 0; i 0) ? (totalPercentageError / mapeValidPeriods) * 100 : NaN;
// Display results
var resultsHTML = "Forecast Accuracy Calculator
Understanding and measuring forecast accuracy is critical for effective business planning, inventory management, resource allocation, and strategic decision-making. This calculator helps you evaluate the performance of your forecasts against actual observed values using several widely recognized metrics.
Why is Forecast Accuracy Important?
- Better Decision Making: Accurate forecasts lead to more informed decisions regarding production, staffing, and marketing.
- Optimized Inventory: Reduces costs associated with overstocking (storage, obsolescence) and understocking (lost sales, customer dissatisfaction).
- Improved Resource Allocation: Ensures that resources (human, financial, material) are deployed efficiently.
- Enhanced Customer Satisfaction: Meeting demand consistently leads to happier customers and stronger brand loyalty.
- Cost Reduction: Minimizes waste, reduces expedited shipping costs, and improves operational efficiency.
Key Forecast Accuracy Metrics Explained
This calculator provides the following metrics:
- Mean Absolute Error (MAE): The average of the absolute differences between the actual and forecasted values. MAE is easy to understand and provides a clear picture of the average magnitude of forecast errors, without considering their direction. A lower MAE indicates higher accuracy.
- Mean Squared Error (MSE): The average of the squared differences between actual and forecasted values. MSE penalizes larger errors more heavily than smaller ones, making it useful when large errors are particularly undesirable.
- Root Mean Squared Error (RMSE): The square root of the MSE. RMSE is often preferred over MSE because it is in the same units as the original data, making it easier to interpret. Like MAE, a lower RMSE signifies better accuracy.
- Bias (Mean Forecast Error): The average of the differences between actual and forecasted values. Bias indicates whether the forecast systematically over-predicts (negative bias) or under-predicts (positive bias) the actual values. A bias close to zero suggests an unbiased forecast.
- Mean Absolute Percentage Error (MAPE): The average of the absolute percentage errors. MAPE expresses accuracy as a percentage, which can be useful for comparing forecast performance across different datasets or items with varying scales. However, MAPE can be problematic when actual values are zero or very close to zero, as it involves division by the actual value.
Calculate Your Forecast Accuracy
Enter the actual observed values and their corresponding forecasted values for each period. You can add or remove periods as needed.
How to Interpret Your Results
- MAE, MSE, RMSE: Lower values are better. These metrics are in the same units as your data (or squared units for MSE), so their "goodness" depends on the scale of your data.
- Bias: A value close to zero is ideal. A positive bias means your forecasts are generally lower than actuals (under-forecasting), while a negative bias means your forecasts are generally higher (over-forecasting).
- MAPE: Lower percentages are better. For example, a MAPE of 5% means your forecasts are, on average, 5% off the actual values. Be cautious with MAPE if your actual values can be zero or very small.
Tips for Improving Forecast Accuracy
- Data Quality: Ensure your historical data is clean, accurate, and relevant.
- Model Selection: Choose forecasting models appropriate for your data patterns (e.g., trend, seasonality).
- Regular Review: Continuously monitor forecast performance and adjust models or parameters as needed.
- Collaboration: Incorporate insights from sales, marketing, and operations teams.
- External Factors: Consider external influences like economic indicators, competitor actions, or market trends.