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The Evolution from Traditional to Machine Learning Forecasting
Why Machine Learning Forecasting Is Better Than Traditional Methods?
How to Implement Machine Learning for Demand Forecasting?
Final Thoughts
Imagine this is a world where we could anticipate future events with uncanny precision, leaving no room for guesswork. Well, that’s not just a dream – it’s a reality thanks to machine learning forecasting. This remarkable process utilizes sophisticated algorithms to learn from data and predict future events accurately.
Big players like Walmart, Hopper, IBM, and Under Armour have already tapped into the power of forecasting to stay ahead of the game. With machine learning models, such as neural networks, we can now analyze vast amounts of data, ushering in a new era of precision in our predictions.
In this article, we’ll uncover everything you need to know about machine learning forecasting, from its transition from traditional methods to remarkable business benefits, and detailed steps to implement it successfully for your organization.
Let’s get the ball rolling!
Various departments within a company engage in forecasting. The finance teams, for example, create revenue forecasts to present potential scenarios, deviations, and openings, enabling executives to optimize outcomes. Staff from supply chain and human resources also rely on precise predictions to anticipate future workforce requirements, aiding in inventory and staffing optimization. Additionally, marketing teams use forecasting to allocate promotional budgets to the highest potential return areas effectively.
Despite the diverse departmental applications, the traditional forecasting process follows a similar pattern: an extensive exercise by technical experts who extract insights from often limited data slices. However, in numerous industries, traditional methodologies have given way to machine learning-based approaches in data and analytics initiatives.
Let’s delve deeper into the remarkable shift from traditional to machine learning forecasting techniques!
Traditional algorithms predominantly rely on predefined techniques and statistical models like linear regression, autoregressive integrated moving averages (ARIMA), and autoregressive integrated moving averages with explanatory variables (ARIMAX). These methods primarily focus on descriptive analysis, dealing with univariate or multivariate datasets featuring finite, countable, and explainable predictors.
The primary aim of traditional forecasting is to estimate future values based on historical business performance data, often including a confidence interval to indicate prediction certainty. In cases where business data is univariate, featuring observations on a single characteristic or variable, traditional statistical methods like those mentioned might yield reasonable forecast accuracy. For instance, to predict the sales of fast-moving consumer goods like dairy products, traditional statistical methods might perform well due to the finite and countable factors influencing sales. However, machine learning algorithms might deliver superior accuracy at the expense of explainability and computing power.
Traditional models effectively handle univariate data with high accuracy. Some classical models include moving average, simple exponential smoothing (SES), Holt-Winters, damped exponential smoothing (DES), linear regression, ARIMA, ARIMAX, and unobserved component modeling. These models are valued for their transparent functioning, offering easily traceable outputs.
The typical traditional forecasting process encompasses the input of historical data, data pre-processing, statistical testing, the application of traditional forecasting algorithms, and finally, the generation of statistical forecast output.
While traditional approaches rely on predefined rules for prediction, machine learning possesses the capacity to acquire knowledge and adapt from an extensive range of data sources. ML forecasting techniques often leverage more intricate features and predictive methodologies. Yet, the primary goal of ML forecasting aligns with that of traditional methods: enhancing forecast precision while minimizing a loss function. The loss function typically involves summating squared errors in forecasting or prediction.
The most notable disparity between these two methodologies emerges in the minimization process. Traditional techniques predominantly rely on interpretable linear processes, whereas machine learning methods frequently employ nonlinear strategies for loss function minimization.
Several examples of machine learning forecasting models applied in business contexts are:
Machine learning techniques demand greater computational resources compared to statistical approaches. In numerous scenarios, the explainability and interpretability of ML models may not be as evident. However, in business applications characterized by extensive data volumes and intricate algorithms, ML methods may excel in predictions due to the multitude of data attributes involved and the non-linearity of the algorithms.
Take the default rate for loan applications as an example. The forecast values can be influenced by thousands of factors based on customer information. In such cases, ML algorithms can surpass statistical methods. An additional advantage of ML forecasting in this context is combining an ensemble of various forecasting techniques, both linear and nonlinear, to achieve higher accuracy.
The machine learning forecasting process follows data acquisition, defining business objectives, data understanding, data cleanup, data preprocessing, feature engineering, dataset segmentation, model building, training, model evaluation, performance comparison, and selecting the model with the least weighted mean absolute percentage error.
From this, it’s evident that the ML forecasting model represents a significant evolution from traditional methods, offering substantial advantages in various aspects.
Let’s read on to gain a better understanding of why this evolution can greatly benefit your business.
Machine learning excels in identifying intricate patterns that surpass human observation. Traditional forecasting methods are constrained by the volume of data processable and analyzable by humans.
For instance, predicting stock market prices needs human analysts to base on market patterns and research for making forecasts. However, the complexity of factors influencing stock prices often eludes human comprehension. In contrast, machine learning swiftly processes extensive data and discerns imperceptible patterns, leading to more precise predictions than traditional methods.
Renaissance Technologies serves as a prime example of machine learning’s success in this domain. Employing machine learning algorithms, the company has achieved over 70% annualized returns since its establishment in 1994.
Machine learning outperforms traditional methods by making predictions based on substantially larger datasets. In forecasting sales, traditional techniques like trend analysis might rely solely on historical sales data. In contrast, machine learning incorporates diverse data sources such as social media, customer reviews, and other information, resulting in more accurate predictions.
Moreover, while traditional methods often focus on time series data alone, machine learning models consider various real-world metrics like supply chain data, enhancing accuracy in demand forecasting. Traditional time series forecasting especially falls short in handling the complexities of big data.
A notable limitation of traditional forecasting methods lies in their susceptibility to biases arising from human emotions and subjective opinions. This predisposition can lead to inaccurate predictions due to human inclinations and feelings. Machine learning, however, operates with lesser influence from human emotions and subjective opinions, thereby contributing to more accurate forecasts.
Consider a scenario where a company wants to open a new store. The personal biases of forecasters might influence traditional forecasting methods, potentially skewing predictions, particularly if they have personal stakes in the outcome. Machine learning, less prone to such biases, generates more precise predictions.
Bear in mind that ML models can still exhibit biases if the training data is biased. However, ensuring the use of unbiased data and employing cross-validation methods can confirm the accuracy of the models.
Machine learning exhibits the remarkable ability to swiftly adapt to changes within a dataset, in stark contrast to traditional methods, which tend to lose accuracy over time as data evolves. The dynamic nature of machine learning ensures that predictions remain consistently accurate and up-to-date, regardless of dataset fluctuations. On the other hand, traditional methods may falter as datasets change.
Think about a dataset containing customer purchase information. Over time, this dataset may undergo alterations as customers change. Traditionally, the approach would require rebuilding forecasts with the updated dataset to yield new predictions. However, machine learning offers the advantage of automatically adapting to the evolving dataset.
Machine learning stands less susceptible to manipulation compared to traditional ways. It uses algorithms to predict, making it more challenging to manipulate these predictions than traditional ones. This inherent resilience enhances the precision of predictions.
Machine learning represents a more resource-efficient approach compared to traditional methods. The latter often demands substantial manual effort, proving time-consuming and costly. Given that modern business leaders always strive for a competitive advantage, harnessing machine learning forecasting can help streamline the prediction process through automation, resulting in a more efficient resource allocation.
Machine learning offers greater accessibility with specialized expertise and training. As technology advances, it becomes increasingly accessible. Numerous software platforms, such as CreateML, DataRobot, Google AutoML, MakeML, and RunwayML, enable individuals to construct a machine learning model without prior knowledge or experience.
Understanding customer needs is paramount for informed decision-making in marketing spending, sourcing, inventory management, production, transportation, and staffing. ML demand forecasting is crucial in this context, including supply planning, product manufacturing planning (e.g., sourcing and R&D), and financial planning.
Notably, ML forecasting methods address traditional challenges like long delivery lead times, high transport costs, excessive inventory and waste, and erroneous decision-making from inaccurate forecasts.
The first thing you have to do is to garner meaningful insights from data through by:
Typically, you need just a few days to grasp the current situation and propose potential solutions.
Next up, clearly define the business goals and success metrics for your machine learning forecasting model. Success metrics offer a clear definition of what is “valuable” within demand forecasting. Consider the following aspects when outlining success metrics:
Different products or services often require independent forecasts. For instance, predicting demand for perishable goods and subscription services occurring at a fixed time will likely differ.
Short-term forecasts usually span less than 12 months, ranging from one week to one month or six months. They serve various purposes: ensuring a consistent product/service supply, setting sales targets, evaluating sales performance, and optimizing prices in response to market fluctuations and inflation.
Long-term forecasts, conversely, encompass periods exceeding one year, primarily intended for long-term financial planning, securing funds, business expansion decisions, annual strategic planning, and related purposes.
The minimum accuracy level depends on your business objectives. Example metrics for measuring forecast accuracy are MAPE (Mean Absolute Percentage Error), MAE (Mean Absolute Error), or custom metrics.
Irrespective of the prediction scope, data quality is pivotal in ensuring an accurate demand forecast. To build forecasting models, utilize various data types like e-commerce sales data, sales transactions, purchase orders, website analytics, reviews, marketing campaign data, app statistics, CRM data, social media inputs, etc.
When developing a forecasting model, the data undergoes evaluation based on these criteria:
In practice, data collected by companies often necessitates cleaning, gap and anomaly analysis, relevance checks, and restoration, typically requiring the assistance of data science consultants.
Once data preparation and structuring are completed, you must comprehend the data. In this phase, data visualization proves to be an effective means to understand the dataset before actual modeling begins.
There is no correct way to choose a machine learning algorithm for forecasting. The choice of machine learning models depends on various factors, including business objectives, data type, data volume and quality, forecasting duration, and more.
However, several approaches have proven to be highly effective for demand forecasting, such as:
With the data and model available, you must train, validate, and enhance the model continuously. This iterative process will continue until the model achieves the desired outcomes.
A forecasting model can be employed in its raw form, delivering predictions in tabular format or through analytics emails for a specified period. However, it’s common to develop a front-end application – a dashboard that offers insights and visualizations through a unified interface. This allows users to query reports, share insights, and customize visualization types. After thorough testing and approval, the product is deployed in the production environment.
To maintain the forecasting model’s efficacy, consistent updates with new data are crucial. These updates may be daily transactions or inventory turnovers. Establishing an automated pipeline for regularly aggregating recent data and updating the ML model can help ensure the reliability of the developed demand forecasting model.
So, there you have it – a comprehensive guide to machine learning forecasting. The transition from traditional methods to machine learning is not just a matter of choice. It’s a leap into the future of informed decision-making. With machine learning, we can identify complex patterns that elude human perception, leverage extensive data for predictions, and reduce the influence of personal biases on forecasts. Moreover, the agility of machine learning to adapt rapidly to changing data and its resistance to manipulation make it a formidable choice.
Ready to take your organization to the next level of demand forecasting? Neurond AI is here to make it happen. Our forecasting and optimization service covers every phase of these workflows, enabling extensive automation to foresee future outcomes swiftly and empowering you to make reliable decisions much faster. So what are you waiting for? Contact us now!
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