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DEMAND FORECAST ACCURACY

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Demand forecasting is essential for businesses to plan effectively and maximize efficiency. Generating highly accurate demand forecasts is extremely challenging due to the many variables that can impact demand. While demand forecasts will never achieve 100% accuracy, forecasters can take steps to improve their forecast accuracy over time.

One of the most important factors that determines forecast accuracy is the choice of forecasting method. There are various quantitative and qualitative forecasting techniques that are more or less suited to different business contexts. Quantitative methods rely on historical data patterns and include simple exponential smoothing, regression analysis, and ARIMA time series analysis. Qualitative techniques incorporate expert opinions, consumer surveys, and indicator data. The appropriate method depends on attributes like a product’s life cycle stage, demand predictability, and data availability. It is usually best to test various methods on historical data to determine which produces the lowest errors for a given situation.

Equally important is having high quality demand history data to feed the forecasting models. Demand data needs to be cleansed of errors, adjusted for factors like price changes or promotions, and segmented appropriately – for example by product, region, or customer type. Missing, inaccurate, or aggregated data can significantly reduce a model’s ability to identify demand patterns. Continuous data quality management processes are required to ensure the inputs yield good forecasts.

Business changes like new product launches, market expansions, or supply constraints also impact demand forecast accuracy. Forecasting models may need to be re-developed when major changes occur since historical demand patterns are unlikely to continue unchanged. Temporary adjustments may help during transitions until new normal demand levels emerge with new historical data. Close collaboration between forecasters and product/supply chain teams ensures such changes are integrated into future forecasts.

Key external variables that are difficult to predict also introduce uncertainties. Economic indicators, competitor actions, new technologies, and weather can all cause demand to deviate from projections. While these macro factors cannot be controlled, forecasters should continuously monitor such variables as much as possible and build scenarios accounting for plausible outcomes. Qualitative inputs from sales, market research, and external data providers help augment quantitative analyses.

Continuous improvement practices help elevate forecast accuracy progressively. Recalibrating forecasting parameters and models based on evaluation of past forecast error patterns helps address known sources of errors. Automated validation and adjustments of prior forecasts based on incoming actual demand data ensures accuracy benefits carry forward. Leveraging advanced techniques like machine learning and partnering with specialist forecasting service providers helps optimize forecasts further. Regular audits reveal ongoing demand changes requiring new forecasting strategies.

Closely involving customers and end users ensures forecasts represent real demand levels and validate assumptions. Gathering timely feedback from customers on order patterns, influencing factors, and future demand indicators helps refine forecasts to anticipate demand shifts. This collaborative approach across functions delivers more demand transparency, allowing issues to be addressed proactively through supply chain readiness or promotion changes, rather than reactively through firefighting shortages or surpluses.

By implementing an integrated approach spanning data quality, forecasting methods, improvement processes and collaboration, businesses can gain significant benefits from higher demand forecast accuracy. While there will always be some unavoidable variation between projections and actual demand, continuous enhancements inch forecasts closer to ground realities over time. This supply chain predictability helps optimize inventory investments, production plans and delivery performance to meet customer expectations.

HOW DO YOU PLAN TO EVALUATE THE ACCURACY OF YOUR DEMAND FORECASTING MODEL?

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To properly evaluate the accuracy of a demand forecasting model, it is important to use reliable and standard evaluation metrics, incorporate multiple time horizons into the analysis, compare the model’s forecasts to naive benchmarks, test the model on both training and holdout validation datasets, and continuously refine the model based on accuracy results over time.

Some key evaluation metrics that should be calculated include mean absolute percentage error (MAPE), mean absolute deviation (MAD), and root mean squared error (RMSE). These metrics provide a sense of the average error and deviation between the model’s forecasts and actual observed demand values. MAPE in particular gives an easy to understand error percentage. Forecast accuracy should be calculated based on multiple time horizons, such as weekly, monthly, and quarterly, to ensure the model can accurately predict demand over different forecast windows.

It is also important to compare the model’s forecast accuracy to some simple benchmark or naive models as a way to establish whether the proposed model actually outperforms simple alternatives. Common benchmarks include seasonal naïve models that forecast based on historical seasonality, or drift models that assume demand will remain flat relative to the previous period. If the proposed model does not significantly outperform these basic approaches, it may not be sophisticated enough to truly improve demand forecasts.

Model evaluation should incorporate forecasts made on both the data used to train the model, as well as newly observed holdout test datasets not involved in the training process. Comparing performance on the initial training data versus later holdout periods helps indicate whether the model has overfit to past data patterns or can generalize to new time periods. Significant degradation in holdout accuracy may suggest the need for additional training data, different model specifications, or increased regularization.

Forecast accuracy tracking should be an ongoing process as new demand data becomes available over time. Regular re-evaluation allows refinement of the model based on accuracy results, helping to continually improve performance. Key areas that could be adapted based on ongoing accuracy reviews include variables included in the model, algorithm tuning parameters, data preprocessing techniques, and overall model design.

When conducting demand forecast evaluations, other useful metrics may include analysis of directional errors to determine whether the model tends to over or under forecast on average, tracking of accuracy over time to identify degrading performance, calculation of error descriptors like skew and kurtosis, and decomposition of total error into systemic versus irregular components. Graphical analysis through forecast error plots and scatter plots against actuals is also an insightful way to visually diagnose sources of inaccuracy.

Implementing a robust forecast accuracy monitoring process as described helps ensure the proposed demand model can reliably and systematically improve prediction quality over time. Only through detailed, ongoing model evaluations using multiple standard metrics, benchmark comparisons, and refinements informed by accuracy results can the true potential of a demand forecasting approach be determined. Proper evaluation also helps facilitate continuous improvements to support high-quality decision making dependent on these forecasts. With diligent accuracy tracking and refinement, data-driven demand modelling can empower organizations through more accurate demand visibility and insightful predictive analytics.

To adequately evaluate a demand forecasting model, reliability metrics should be used to capture average error rates over multiple time horizons against both training and holdout test data. The model should consistently outperform naive benchmarks and its accuracy should be consistently tracked and improved through ongoing refinements informed by performance reviews. A thoughtful, methodical evaluation approach as outlined here is required to appropriately determine a model’s real-world forecasting capabilities and ensure continuous progress towards high prediction accuracy.

HOW WOULD THE STUDENTS EVALUATE THE ACCURACY OF THE DIFFERENT FORECASTING MODELS

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The students would need to obtain historical data on the variable they are trying to forecast. This could be things like past monthly or quarterly sales figures, stock prices, weather data, or other time series data. They would split the historical data into two parts – a training set and a testing set.

The training set would contain the earliest data and would be used to develop and train each of the forecasting models. Common models students may consider include simple exponential smoothing, Holt’s linear trend method, Brown’s exponential smoothing approach, ARIMA (autoregressive integrated moving average) models, and regression models with lagged predictor variables. For each model, the students would select the optimal parameters like the alpha level in simple exponential smoothing or the p, d, q parameters in ARIMA.

Once the models have been developed on the training set, the students would then forecast future periods using each model but only using the information available up to the end of the training set. These forecasts would be compared to the actual data in the testing set to evaluate accuracy. Some common metrics that could be used include:

Mean Absolute Percentage Error (MAPE) – This calculates the average of the percentage errors between each forecast and the actual value. It provides an easy to understand measure of accuracy with a lower score indicating better forecasts.

Mean Absolute Deviation (MAD) – Similar to MAPE but without calculating the percentage, instead just looking at the average of the absolute errors.

Mean Squared Error (MSE) – Errors are squared before averaging so larger errors are weighted more heavily than small errors. This focuses evaluation on avoiding large forecast misses even if some smaller errors occur. MSE needs to be interpreted carefully as the scale is not as intuitive as MAPE or MAD.

Mean Absolute Scaled Error (MASE) – Accounts for the difficulty of the time series by comparing forecast errors to a naive “random walk” forecast. A MASE below 1 indicates the model is better than the naive forecast.

The students would calculate accuracy metrics like MAPE, MAD, MSE, and MASE for each model over the test period forecasts. They may also produce graphs to visually compare the actual values to each model’s forecasts to assess accuracy over time. Performance could also be evaluated at different forecast horizons like 1-period ahead, 3-period ahead, 6-period ahead forecasts to see if accuracy degrades smoothly or if some models hold up better farther into the future.

Additional analysis may include conducting Diebold-Mariano tests to statistically compare model accuracy and determine if differences in the error metrics between pairs of models are statistically significant or could be due to chance. They could also perform residual diagnostics on the forecast errors to check if any patterns remain that could be exploited to potentially develop an even more accurate model.

After comprehensively evaluating accuracy over the test set using multiple error metrics and statistical comparisons, the students would identify which forecasting model or models provided the most accurate and reliable forecasts based on the historical data available. No single metric alone would determine the best model, but rather the preponderance of evidence across the board in terms of MAPE, MAD, MSE, MASE, visual forecasts, statistical tests, and residual analysis.

The students would report their analysis, including details on developing each model type, describing the accuracy metrics calculated, presenting the results visually through tables and graphs, discussing their statistical findings, and making a conclusion on the most accurate model indicated by this thorough ex-post evaluation process. This would provide them significant insight into forecasting, model selection, and evaluation that they could apply in practice when working with real time-series data challenges.

While accuracy alone cannot guarantee a model’s future performance, this process allows the students to rigorously benchmark the performance of alternative techniques on historical data. It not only identifies the empirical ex-post leader, but also highlights how much more accurate or less accurate other methods were so they can better understand the practical value and predictive limitations of different approaches. This in-depth workflow conveys the types of analysis real-world data scientists and business analysts would carry out to select the optimal forecasting technique.

WHAT OTHER FACTORS COULD POTENTIALLY IMPROVE THE ACCURACY OF THE GRADIENT BOOSTING MODEL?

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Hyperparameter tuning is one of the most important factors that can improve the accuracy of a gradient boosting model. Some key hyperparameters that often need tuning include the number of iterations/trees, learning rate, maximum depth of each tree, minimum observations in the leaf nodes, and tree pruning parameters. Finding the optimal configuration of these hyperparameters requires grid searching through different values either manually or using automated techniques like randomized search. The right combination of hyperparameters can help the model strike the right balance between underfitting and overfitting to the training data.

Using more feature engineering to extract additional informative features from the raw data can provide the gradient boosting model with more signals to learn from. Although gradient boosting models can automatically learn interactions between features, carefully crafting transformed features based on domain knowledge can vastly improve a model’s ability to find meaningful patterns. This may involve discretizing continuous variables, constructing aggregated features, imputing missing values sensibly, etc. More predictive features allow the model to better separate different classes/targets.

Leveraging ensemble techniques like stacking can help boost accuracy. Stacking involves training multiple gradient boosting models either on different feature subsets/transformations or using different hyperparameter configurations, and then combining their predictions either linearly or through another learner. This ensemble approach helps address the variance present in any single model, leading to more robust and generalized predictions. Similarly, random subspace modeling, where each model is trained on a random sample of features, can reduce variability.

Using more training data, if available, often leads to better results with gradient boosting models since they are data-hungry algorithms. Collecting more labeled examples allows the models to learn more subtle and complex patterns in large datasets. Simply adding more unlabeled data may not always help; the data need to be informative for the task. Also, addressing any class imbalance issues in the training data can enhance model performance. Strategies like oversampling the minority class may be needed.

Choosing the right loss function suited for the problem is another factor. While deviance/misclassification error works best for classification, other losses like Huber/quantilic optimize other objectives better. Similarly, different tweaks like softening class probabilities with logistic regression in the final stage can refine predictions. Architectural choices like using more than one output unit enable multi-output or multilabel learning. The right loss function guides the model to learn patterns optimally for the problem.

Carefully evaluating feature importance scores and looking for highly correlated or redundant features can help remove non-influential features pre-processing. This “feature selection” step simplifies the learning process and prevents the model from wasting capacity on unnecessary features. It may even improve generalization by reducing the risk of overfitting to statistical noise in uninformative features. Similarly, examining learned tree structures can provide intuition on useful transformations and interactions to be added.

Using other regularization techniques like limiting the number of leaves in each individual regression tree or adding an L1 or L2 penalty on the leaf weights in addition to shrinkage via learning rate can guard against overfitting further. Tuning these regularization hyperparameters appropriately allows achieving the optimal bias-variance tradeoff for maximum accuracy on test data over time.

Hyperparameter tuning, feature engineering, ensemble techniques, larger training data, proper loss function selection, feature selection, regularization, and evaluating intermediate results are some of the key factors that if addressed systematically can significantly improve the test accuracy of gradient boosting models on complex problems by alleviating overfitting and enhancing their ability to learn meaningful patterns from data.