Time series forecasting of essential business metrics like sales, inventory, and demand is becoming critical for every organization. Time-stamped data can be effective drivers of your business strategy and decision making.
However, time series forecasts can in fact be plagued by several errors. Forecasting by definition can’t give you exact predictions, and can even fluctuate significantly due to factors well beyond your control. But the more precisely you build your forecasting models according to what you *can* control, the greater the confidence you can put into the predictions that come from them.
Let’s get you some tips on how to reduce errors and improve your forecasts. But first, let me put a bit more emphasis on why accurate time series forecasting is so important anyway.
Accurate forecasts provide insights about a business’s possible future performance. These insights empower leaders to make strategic decisions with greater confidence, which is especially helpful in highly dynamic and uncertain business environments like finance. In fact, several business functions critically depend on the availability of forecasts, like sales, marketing, supply chain, and pricing.
Accurate forecasting is also an opportunity for analytics and business leaders to learn from past mistakes. When you compare forecasts for a certain period with the actual data from the same time period, you get valuable feedback. This feedback not only relates to the performance of your forecasting models but also the quality and impact of business decisions based on them. Wise leaders leverage that insight into improvements for both the time series forecasting models and business strategy.
Several factors that lead to poor forecasting are fortunately preventable, not least of which is human error.
Despite the availability of several state-of-the-art forecasting models, several organizations still make forecasts manually, either due to a lack of resources or skill for business analytics. Manual forecasting introduces errors and biases that ultimately affect the quality of business strategy and decisions.
Another reason for poor forecasting is the use of limited forecasting techniques. Many companies still use rule-based methods for forecasting and have yet to embrace statistical, machine learning, and deep learning techniques. With more advanced techniques, your organization can improve upon the accuracy of existing forecasting methods to deliver great return on investment.
For several organizations, the only option is using third-party tools for their forecasting needs. However, ineffective software can lead to poor forecasting; without the ability to optimize the tool for a business’s specific characteristics, any insights garnered from these forecasts will be incomplete at best.
While perfect forecasting is never a guarantee, you can greatly improve the quality and accuracy of time series forecasts in three steps.
The most impactful recommendation is to automate the entire process of producing your forecasts.
If your organization is planning to move from manual to automated forecasting, you’re probably already aware that it will take a substantial one-off investment of time, resources, and bandwidth across most of your organization. To automate the entire pipeline from raw data to forecasts to business insights, you’ll need a preliminary analysis of the particular time series data for each use case. Once you understand the statistical distribution of your data, you can select an appropriate time series forecasting method.
Automating your forecasts allows the business analytics team to produce forecasts with less effort, which means they can spend more time interpreting the results to generate critical business insights.
Automated forecasts are also simply less painful to produce. If your organization has shorter business planning cycles, automation makes a weekly or monthly forecasting cadence quite feasible, instead of the traditional quarterly or annual planning process. As a result, your organization can be more agile, respond to changes in industry more quickly, and update business strategy accordingly.
Once you have automated your forecasting pipeline with a particular method, you can begin looking around at different forecasting models, including:
There is no definitive “best” forecasting model, of course, given that your specific use case will differ from someone else’s. Rigorous experimentation and evaluation is important to identify the model that is best suited for each business application, whether it is sales forecasting or demand planning.
If that feels like a lot of pressure, keep in mind that your choice of forecasting model need not be fixed for all time. As the nature of the underlying data distribution changes, some models may no longer be the most optimal method. Cultivate a culture of data-driven experimentation in your organization, so your decision-making can be flexible in times of change.
If your organization has decided to use third-party software for time series forecasting, ensure that the tool you’ve selected is reliable and yields robust and accurate forecasts.
An effective third-party forecasting tool should be easy to use by both data analysts as well as business stakeholders. Estimates should be clear and confident. Be prepared to evaluate the software for its technical rigor, frequency of product updates, and prompt and efficient customer service.
There are lots of factors outside your control that will affect time series forecasting. But even though you’re not in control, it’s still critical to consider them.
The relationships between the forecasted values and corresponding features may not be constant. For instance, black swan events like the Covid-19 pandemic can completely change the relationship between input features and forecasts. It’s important to constantly examine the statistical properties of your data and update forecasting models accordingly.
External market data and indicators aren’t within your control, but they can still contribute to improving the time series forecasts. For instance, measures of inflation, like the consumer price index, affect customer demand and may significantly alter the accuracy of your forecasts. This is especially pertinent since 2020; drastic macroeconomic changes have had serious, lengthy impact, such as high inflation and recessionary markets.
Finally, don’t forget the people around you. It’s essential to get relevant stakeholders and leaders involved in the process of determining metrics and drivers relevant to the forecasts. This not only helps build buy-in for automated forecasting and new tooling, but insights from other roles may be crucial to consider in your forecasting models.
Time series forecasting is obviously a critical support for all levels of business strategy, from managing inventory to responding to changing customer demand. It is therefore essential to understand the reasons underlying poor forecasting models and then take steps to improve how your business conducts its forecasting.
While you can build time series forecasting models from scratch using libraries in R, Python, and so on, for many organizations, it makes more sense to leverage existing platform solutions. For example, Ikigai is an operational AI apps platform whose forecasting model, aiCast, fits all sizes of datasets and provides highly granular analysis of business insights.
Thanks to its no-code approach, Ikigai is easy for analysts and non-technical stakeholders to use. Book a demo today to learn how you can automate and improve the accuracy of your time series forecasts with Ikigai.