Moving holidays are holidays that occur each year, but where the exact timing shifts from the perspective of the Gregorian calendar system. Chinese New Year (CNY) is an example of a moving holiday, it is based on the lunar calendar. Chinese New Year most often falls in February but can also occur in January. Since the date of Chinese New Year changes from year to year, the effect of this holiday can impact sales in multiple months. It is often the case that production accelerates some time before the start of Chinese New Year, almost completely stops during the holidays, and finally rises to the regular level after the holidays. In these cases, the effect of the holiday is not confined to the seasonal component of the time series since the seasonality rhythm (based on the lunar calendar) is not in line with the demand forecast rhythm (based on the Gregorian calendar). This often leads to a significant decrease in the performance of the statistical forecast (i.e. lower forecasting accuracy and higher bias) for the months affected by the holiday.

Combining statistical forecasting with machine learning for increased forecasting accuracy

Conventional statistical models (e.g. moving average and exponential smoothing) are widely used within the industry to predict demand. Often with good reason, since these models usually perform reasonably well and they are intuitive and easy to interpret for planners. We have seen, that statistical models (even if we add a seasonal component) are not able to model the complex effects of moving holidays. Based on these observations, we designed a new approach using machine learning to predict ‘uplift’ factors that are used to scale the baseline forecasts. We give a detailed description on how EyeOn addresses this challenge in this blog post: How to make better demand forecasts for Chinese New Year using machine learning

Results case study previous blog

Going beyond Chinese New Year

The results of modeling Chinese New Year for a large multinational are very promising. In this case study, we find enormous improvements in forecast bias. For ten different regions, we achieve an average absolute bias reduction of 68 percent points. We don’t stop there, however! The next step is to extend the logic on two fronts: (1) automatic detection of significant events (rather than manual) and (2) applying the logic to a wider variety of moving holidays in addition to Chinese New Year.

First, we implement automated event detection to determine whether an event significantly impacts a time series in a certain period. If so, we use our machine learning model to apply a correction to the baseline forecast. If not, we just use our statistical baseline forecast. The event detection consists of three statistical tests, which determine if the sales during an event are significantly impacted. The identified events are corrected by predicting uplift factors for the event periods. Automatic event detection results in less manual intervention from the forecaster and provides statistical proof that an event has an impact on the time series. Making you less dependent on the forecaster’s ability to interpret and their experience of whether a correction should be applied.

Second, besides the uplift factors, the event sales are corrected to improve the forecast performance in the periods after the event. We do this correction to avoid the sales disruption during the event impacting the statistical baseline forecast. In the case of Chinese New Year, the correction is used to make sure that the statistical forecast level will not be too low after having three negatively impacted sales months.

The extension of this logic makes it possible to better forecast moving holidays and to rely on statistics to determine which events should be corrected. Making it applicable for more customers and events, resulting in improved forecasts during moving holidays. We applied the new extended logic to two other events besides Chinese New Year in a new case study. Comparing the results of the developed method to the results of conventional time series models (e.g. moving average/simple exponential smoothing). In this case, we find an average increase of 13.1 percentage points in forecasting accuracy and a reduction of 18.7 percentage points in the bias for the affected months. Note that for the non-impacted months, the two forecasts are identical.

Challenge accepted, challenge completed: increased forecasting accuracy

Moving holidays can have a significant impact on demand. Moreover, modeling this effect can be challenging since the effect of the event impacts a different period (e.g. week or month) each year. We accepted this challenge and developed a new approach where the conventional time series forecast is complemented with a machine learning algorithm that models the effect of moving holidays. This approach is now available for a large variety of events and customers.

Eager to learn more about this topic or curious if this method could also be valuable for your organization? Please reach out to us!

The post Increased forecasting accuracy during moving holidays appeared first on EyeOn.

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Moving holidays are holidays that occur each year, but where the exact timing shifts from the perspective of the Gregorian calendar system. Examples of moving holidays include Easter and Chinese New Year (CNY). Easter generally falls in April but can also fall in late March. Chinese New Year mostly falls in February but can also occur in January. Since the date of these holidays changes from year to year, their effect can impact two or more months depending on the date. Related to Chinese New Year, for example, it is often the case that production accelerates some time before the start of Chinese New Year, almost completely stops during the holidays, and finally rises to the regular level after the holidays. In these cases, we don’t talk about seasonality in forecasting, because the effect of the holiday is not confined to the seasonal component of the time series since the seasonality rhythm (based on the lunar calendar) is not in line with the demand forecast rhythm (based on the Gregorian calendar). This often leads to a significant decrease in the performance of the statistical forecast (i.e. lower accuracy and higher bias) for the months affected by the holiday. In this blog, we will explore how EyeOn addresses this forecasting seasonality challenge for one of our customers.

Combining conventional statistical forecasting with machine learning

Conventional statistical models (e.g. moving average and exponential smoothing) are widely used within the industry to predict demand. Often with good reason, since these models usually perform reasonably well and are intuitive and easy to interpret for planners. We have seen, however, that these models (even if we add a seasonality component) are not able to model the complex effects of moving holidays. Based on these observations, we designed the following approach at EyeOn:

1. Start with a conventional statistical model to obtain a baseline forecast.
2. Predict “uplift” factors for months affected by the moving holiday(s). In other words, in this step, we are estimating how much higher or lower the demand is in a given month, relative to the baseline demand. Note that the uplift factor can also be smaller than 1. In that case, we are actually scaling down the baseline forecast.
3. Multiply the baseline forecast with the uplift factors to obtain the final forecast.

Obviously, this approach relies to a large extent on how well we are able to estimate the uplift factors, and for this step, we are harvesting the power of machine learning. In the context of modeling moving holidays effects, a well-known type of regressor called the Bell-Hillmer interval, has proven to be very useful. Assuming that the holiday effect is the same for each day of the interval over which the regressor is nonzero in a given year, the value of the regressor in a given month is the proportion of this interval that falls in the month. Using this logic, we can thus define multiple intervals to model the backward and forward effect of a moving holiday. These Bell-Hillmer regressors are used as features in a machine-learning algorithm that uses gradient boosting on decision trees.
Although the above description might sound daunting at first, what it essentially boils down to is this: based on the characteristics of one or more moving holidays, we let a smart algorithm learn by which factor we need to adjust the statistical baseline forecast.

Case study on forecasting seasonality: modeling the impact of Chinese New Year for a large multinational

Chinese New Year is China’s most important holiday and the largest annual mass migration on the planet. Since most elderly parents live in rural villages and their children work in the cities, the “chunyun” (spring migration) creates approximately two to four weeks of radio silence from the entire country, including your suppliers, contract manufacturers, and partners. During this time, almost everything shuts down.
All of this poses serious complex supply chain planning challenges for all companies operating in Asia. The graph below shows one of these challenges. The vertical bars represent the sales quantity per month from January 2017 up until March 2021. The orange-shaded bars annotate the months December, January, and February where the effect of CNY is clearly visible. Also, note that the effect varies considerably from year to year. For instance, in the years on which CNY fell in January (2017 and 2020), the sales in January were impacted significantly more compared to the years when CNY fell in February. The blue line in the graph depicts the forecast generated by conventional time series models (i.e. moving average/simple exponential smoothing). Note that, although we supplemented these models with a seasonal component, they do not fully capture the effect of CNY. This results in reduced forecast accuracy and greatly increased bias for the months affected by CNY.
The green line shows the forecast as generated by the method proposed in this blog. Already from looking at the graph, it becomes clear that this forecast outperforms the conventional time series forecast. In this case, we found an increase of 3.5 percentage points in forecast accuracy and a reduction of 44.6 percentage points in the bias. Note that for the non-impacted months, the two forecasts are identical.

Final thoughts on forecasting seasonality

While conventional time series models have a good track record and are the de facto standard in the industry, they are often not equipped to capture more complex effects, such as moving holidays. If these effects are large (such as in our example with Chinese New Year), this could lead to diminishing performance of the statistical forecast. To address this issue, we implement this new forecast approach where the conventional time series forecast is complemented with a machine learning algorithm that models the effect of moving holidays.

Eager to learn more about this topic or curious if this method could also be valuable for your organization? Please reach out to us: Rijk van der Meulen, Anne de Vries, or Dan Roozemond.

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