In this blog, we will analyze the automatic forecasting for time series demand projections, focusing on key techniques, challenges, and best practices. There are multiple methods to predict future demand for an item, and this becomes complex when dealing with thousands of items, each requiring a different forecasting technique due to their unique demand patterns. Some items have stable demand, others trend upwards or downwards, and some exhibit seasonality. Selecting the right method for each item can be overwhelming. Here, we’ll explore how automatic forecasting simplifies this process.

Automatic forecasting becomes fundamental in managing large-scale demand projections. With thousands of items, manually selecting a forecasting method for each is impractical. Automatic forecasting uses software to make these decisions, ensuring accuracy and efficiency in the forecasting process. It’s importance lies in its ability to handle complex, large-scale forecasting needs efficiently. It eliminates the need for manual selection, saving time and reducing errors. This approach is particularly beneficial in environments with diverse demand patterns, where each item may require a different forecasting method.

Key Considerations for Effective Forecasting

1. Challenges of Manual Forecasting:
   • Infeasibility: Manually choosing forecasting methods for thousands of items is unmanageable.
   • Inconsistency: Human error can lead to inconsistent and inaccurate forecasts.
2. Criteria for Method Selection:
   • Error Measurement: The primary criterion for selecting a forecasting method is the typical forecast error, defined as the difference between predicted and actual values. This error is averaged over the forecast horizon (e.g., monthly forecasts over a year).
   • Holdout Analysis: This technique simulates the process of waiting for a year to elapse by hiding some historical data, making forecasts, and then revealing the hidden data to compute errors. This helps in choosing the best method in real-time.
3. Forecasting Tournament:
   • Method Comparison: Different methods compete to forecast each item, with the method producing the lowest average error winning.
   • Parameter Tuning: Each method is tested with various parameters to find the optimal settings. For example, simple exponential smoothing may be tried with different weighting factors.

The Algorithms Behind Effective Automatic Forecasting

Automatic forecasting is highly computational but feasible with modern technology. The process involves:

• Data Segmentation: Dividing historical data into segments helps manage and leverage different aspects of historical data for more accurate forecasting. For instance, for a product with seasonal demand, data might be segmented by seasons to capture season-specific trends and patterns. This segmentation allows forecasters to make and test forecasts more effectively.
• Repeated Simulations: Using sliding simulations involves repeatedly testing and refining forecasts over different periods. This method validates the accuracy of forecasting methods by applying them to different segments of data. An example is the sliding window method, where a fixed-size window moves across the time series data, generating forecasts for each position to evaluate performance.
• Parameter Optimization: Parameter optimization involves trying multiple variants of each forecasting method to find the best-performing one. By adjusting parameters, such as the smoothing factor in exponential smoothing methods or the number of past observations in ARIMA models, forecasters can fine-tune models to improve performance.

For instance, in our software, we allow various forecasting methods to compete for the best performance on a given item.  Knowledge of Automatic forecasting immediately carries over to Simple Moving Average, linear moving average, Single Exponential Smoothing, Double Exponential Smoothing, Winters’ Exponential Smoothing, and Promo forecasting. This competition ensures that the most suitable method is selected based on empirical evidence, not subjective judgment. The tournament winner is the closest method to predicting new data values from old. Accuracy is measured by average absolute error (that is, the average error, ignoring any minus signs). The average is computed over a set of forecasts, each using a portion of the data, in a process known as sliding simulation, which we have explained previously in a previous blog.

Methods used in Automatic forecasting

Normally, there are six extrapolative forecasting methods competing in the Automatic forecasting tournament:

• Simple moving average
• Linear moving average
• Single exponential smoothing
• Double exponential smoothing
• Additive version of Winters’ exponential smoothing
• Multiplicative version of Winters’ exponential smoothing

The latter two methods are appropriate for seasonal series; however, they are automatically excluded from the tournament if there are fewer than two full seasonal cycles of data (for example, fewer than 24 periods of monthly data or eight periods of quarterly data). These six classical, smoothing-based methods have proven themselves to be easy to understand, easy to compute and accurate. You can exclude any of these methods from the tournament if you have a preference for some of the competitors and not others.

Automatic forecasting for time series data is essential for managing large-scale demand projections efficiently and accurately. Businesses can achieve better forecast accuracy and streamline their planning processes by automating the selection of forecasting methods and utilizing techniques like holdout analysis and forecasting tournaments. Embracing these advanced forecasting techniques ensures that businesses stay ahead in dynamic market environments, making informed decisions based on reliable data projections.

​When you should use traditional extrapolative forecasting techniques.

With so much hype around new Machine Learning (ML) and probabilistic forecasting methods, the traditional “extrapolative” or “time series” statistical forecasting methods seem to be getting the cold shoulder.  However, it is worth remembering that these traditional techniques (such as single and double exponential smoothing, linear and simple moving averaging, and Winters models for seasonal items) often work quite well for higher volume data. Every method is good for what it was designed to do.  Just apply each appropriately, as in don’t bring a knife to a gunfight and don’t use a jackhammer when a simple hand hammer will do. 

Extrapolative methods perform well when demand has high volume and is not too granular (i.e., demand is bucketed monthly or quarterly). They are also very fast and do not use as many computing resources as probabilistic and ML methods. This makes them very accessible.

Are the traditional methods as accurate as newer forecasting methods?  Smart has found that extrapolative methods do very poorly when demand is intermittent. However, when demand is higher volume, they only do slightly worse than our new probabilistic methods when demand is bucketed monthly.  Given their accessibility, speed, and the fact you are going to apply forecast overrides based on business knowledge, the baseline accuracy difference here will not be material.

The advantage of more advanced models like Smart’s GEN2 probabilistic methods is when you need to predict patterns using more granular buckets like daily (or even weekly) data.  This is because probabilistic models can simulate day of the week, week of the month, and month of the year patterns that are going to be lost with simpler techniques.  Have you ever tried to predict daily seasonality with a Winter’s model? Here is a hint: It’s not going to work and requires lots of engineering.

Probabilistic methods also provide value beyond the baseline forecast because they generate scenarios to use in stress-testing inventory control models. This makes them more appropriate for assessing, say, how a change in reorder point will impact stockout probabilities, fill rates, and other KPIs. By simulating thousands of possible demands over many lead times (which are themselves presented in scenario form), you’ll have a much better idea of how your current and proposed stocking policies will perform. You can make better decisions on where to make targeted stock increases and decreases.

So, don’t throw out the old for the new just yet. Just know when you need a hammer and when you need a jackhammer.