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What are the best ways to test the accuracy of your forecasting model?

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Forecasting is a crucial skill for market researchers, as it helps them anticipate future trends, demand, and opportunities. However, forecasting is not an exact science, and it requires constant testing and validation to ensure its accuracy and reliability. In this article, we will explore some of the best ways to test the accuracy of your forecasting model, and how to improve it over time.

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Dominica E.

Business Consultant & Strategist | Driving Growth through Innovation & Global Insights | Passionate About Emerging…
Albaraa Al-Thami, MSc

Driving Customer-Obsessed Success Stories ?? | Retail Growth Strategy Visionary?? | Crafting Customer-Centric Growth |…

1 Use historical data

One of the simplest and most effective ways to test the accuracy of your forecasting model is to use historical data as a benchmark. By comparing your model's predictions with the actual outcomes that occurred in the past, you can measure how well your model captures the patterns, fluctuations, and seasonality of your data. You can also identify any gaps, biases, or errors in your model, and adjust it accordingly. Historical data can be used to test your model both in-sample and out-of-sample, meaning within and outside the time period that was used to train your model.

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Albaraa Al-Thami, MSc

Driving Customer-Obsessed Success Stories ?? | Retail Growth Strategy Visionary?? | Crafting Customer-Centric Growth | Franchise Expert & Frontline Regional Leader | Marketing Analytics Expert
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In my experience, historical data is just a starting point. Watch out for unexpected events - those black swans can throw even the best model off course. Pro tip: Use historical data to create "what-if" scenarios for potential disruptions. It'll help you adapt your forecasts and stay ahead of the curve. ??

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2 Apply error metrics

Another way to test the accuracy of your forecasting model is to apply error metrics that quantify the difference between your model's predictions and the actual outcomes. There are many error metrics that you can use, depending on your data and objectives, such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE). These metrics can help you evaluate how close your model is to the true values, and how consistent and reliable it is across different time periods and scenarios.

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Dominica E.

Business Consultant & Strategist | Driving Growth through Innovation & Global Insights | Passionate About Emerging Tech, Business Development, and Branding | Global Citizen
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Different metrics can be used to quantify the accuracy of forecasts. Common metrics include: - Mean Absolute Error (MAE): Measures the average magnitude of errors in a set of predictions, without considering their direction. - Mean Squared Error (MSE): Measures the average of the squares of the errors. It gives a higher weight to larger errors. - Root Mean Squared Error (RMSE): Square root of MSE. It is useful when large errors are particularly undesirable. - Mean Absolute Percentage Error (MAPE): Measures the size of the error in percentage terms. It’s especially useful when you need to compare the accuracy of models across different data scales.

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3 Perform sensitivity analysis

A third way to test the accuracy of your forecasting model is to perform sensitivity analysis, which involves changing one or more input variables or parameters of your model and observing how it affects the output. Sensitivity analysis can help you understand how robust your model is to changes in the data, assumptions, or conditions, and how sensitive it is to uncertainty and variability. Sensitivity analysis can also help you identify the most influential factors that affect your forecast, and how to prioritize them in your model.

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4 Use cross-validation

A fourth way to test the accuracy of your forecasting model is to use cross-validation, which is a technique that splits your data into multiple subsets and uses some of them to train your model and others to test it. Cross-validation can help you avoid overfitting, which is when your model performs well on the training data but poorly on new or unseen data. Cross-validation can also help you optimize your model's parameters and select the best model among different alternatives.

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5 Seek feedback

A fifth way to test the accuracy of your forecasting model is to seek feedback from experts, stakeholders, or customers who have relevant knowledge or experience in your domain. Feedback can help you validate your model's assumptions, logic, and results, and provide you with insights, suggestions, or criticisms that can improve your model. Feedback can also help you communicate your model's strengths, limitations, and uncertainties, and build trust and confidence in your forecast.

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6 Update your model

A final way to test the accuracy of your forecasting model is to update it regularly with new data, information, or feedback. Updating your model can help you capture the latest trends, changes, and events that affect your forecast, and adjust your model accordingly. Updating your model can also help you monitor its performance over time, and detect any deviations, anomalies, or errors that need to be corrected. Updating your model can help you maintain its accuracy and relevance, and ensure that it reflects the current and future reality of your market.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Dominica E.

Business Consultant & Strategist | Driving Growth through Innovation & Global Insights | Passionate About Emerging Tech, Business Development, and Branding | Global Citizen
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Sometimes, the ultimate test of a forecasting model is how it performs in the real world. This involves deploying the model in a live environment and monitoring its predictions over time. Each of these methods provides a different lens through which to view the model's performance, and often, a combination of these methods is used to get a comprehensive understanding of the model's accuracy and robustness.

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What are the best ways to test the accuracy of your forecasting model?

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1 Use historical data

2 Apply error metrics

3 Perform sensitivity analysis

4 Use cross-validation

5 Seek feedback

6 Update your model

7 Here’s what else to consider

Market Research

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What are the best ways to test the accuracy of your forecasting model?

1

2

3

4

5

6

7

1 Use historical data

2 Apply error metrics

3 Perform sensitivity analysis

4 Use cross-validation

5 Seek feedback

6 Update your model

7 Here’s what else to consider

Market Research

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