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Anastasiia Molodoria
Anastasiia Molodoria

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Machine Learning Forecasting for Enhancing Business Intelligence

Business forecasting is imperative for making balanced financial and operational decisions. Its impact across industries has grown in recent years due to the way companies build data-driven strategies and rely on data. But let’s find out what is needed for efficient forecasting and why machine learning models have all the prerequisites for enhancing business intelligence.

In this article, we’ll go over the principles of ML forecasting functioning and the benefits it can bring if used for business purposes. Also, we will highlight the differences between machine learning forecasting models, from regression to exponential smoothing.

How AI Improves Business Forecast Accuracy

Thanks to forecasting, companies are able to better serve clients and ship orders, instead of running out of stock. This leads to a huge impact on sales and customer satisfaction. For example, knowing the demand brings an ability to manage logistics and track inventory costs, or even predict ROI for a new product. Therefore, ML forecasting models allow organizations to enhance their AI maturity, and more importantly, to solve business tasks by looking at existing data.

Nowadays, the volume of data from markets, industries, and users is skyrocketing. FinancesOnline reveals that the world will produce and consume 94 zettabytes in 2022. Such growth fuels the training of ML models, making them more robust and accurate. According to Market Research Future, the ML market share is projected to reach $106.52B by 2030, with a CAGR of 38.76% during the forecast period of 2020-2030. With increasing market share (caused by evolving cloud-based services and growth in unstructured data) comes new opportunities for building forecasting models. So, let’s figure out how these models improve business forecast accuracy and why they are more efficient than traditional approaches.

ML forecasting rests on an enormous amount of information, which can be analyzed to achieve accurate predictions and high performance rates. Unlike traditional forecasting approaches, machine learning allows companies to consider numerous business drivers and factors, and for building nonlinear algorithms to minimize loss functions (a crucial ingredient in all optimization problems).

Training of any ML forecasting model requires the assessment stage. This stage foresees comparison of predicted and actual results. It brings an understanding of how well the model performs. After that, it would be possible to compare different forecasting algorithms and choose the one which produces a minimal amount of errors. With this approach, businesses can replace traditional techniques with ML, getting the following benefits for their business forecast:

  • Acquiring insights and detecting hidden patterns that are difficult to trace with traditional approaches. Training ML forecasting models on BigData, and moving computation to Cloud is becoming de-facto an industry standard.
  • Reduced number of errors in forecasting. For instance, McKinsey claims that AI-driven forecasting models applied to delivery chain management can reduce the number of errors by 20–50%.
  • Ability to infuse more data in a model. External data may be valuable here and change the outcomes in terms of predictions.
  • Flexibility and rapid adaptability to changes. Compared to traditional non-AI approaches, ML forecasting algorithms can be quickly adapted in case of any significant changes.

Please note that we’re considering forecasting, not predictive modeling. We’ll explain the difference between these two models in simple terms.

Difference Between Forecasting & PredictiveModeling

Both forecasting and predictive algorithms are applied to address cumbersome challenges related to business planning, customer behavior, and decision-making. But, nevertheless, these techniques differ.

Forecasting modeling implies analysis of past and present data to find patterns, or trends, which allow us to estimate the probability of future events. In contrast to predicting, forecasting modeling should have traceable logics. Typical use cases include a forecast for energy consumption in the following 6–12 months, an evaluation of how many customers will reach support in the next 7 days, or how many agreements for the supply are expected to be signed. All this could be forecasted based on previous (historical) data.

Predictive modeling is the process of applying AI and data mining to assess more detailed, specific outcomes and use much more diverse data types. The difference between predictive and forecasting modeling is blurred, still, we can consider an example to understand it better. Just imagine that a credit institution plans to launch a new premium card. At this point, two questions may arise.

The first will probably be, how many cards will be issued in the next 6 months? Forecasting modeling will help us find an answer to this question thanks to analysis of similar products launched in the past.But we still don’t know whom we can recommend this card to. Here predictive modeling comes into play. It enables us to analyze a customer information database with such fields as age, salary, preferences, consumer habits, etc. With this approach, we will eventually understand which clients are more likely to use this card.

Use Сases For Machine Learning Forecasting For Business


Without a financial forecast, companies face disruption in processes and performance, while C-level managers tend to make incorrect decisions. That’s why companies leverage ML forecasting which instead of dealing with mundane tasks, concentrates attention on understanding business drivers. Moreover, ML financial forecasting reduces the amount of ineffective strategies in play and human errors and helps predict supply, demand, inventory, future revenues, expenses, and cash flow.

For example, stakeholders of the business are aiming to know the company’s turnover and key factors for growth during the next financial period to understand and analyze areas of improvement. Based on historical key company business indicators and existing turnover information during the past periods, we can develop an ML forecasting model using deep learning or regression models. It will predict future required metrics, based also on seasonal information and other influencing factors. In this case, business owners will be able to plan the next period of time accordingly.


ML can fully transform management in the area of supply chains, which are becoming more globalized and sophisticated. ML-based forecasting solutions enable companies to efficiently respond to issues and threats as well as avoid under and overstocking. Machine learning algorithms for forecasting can learn relationships from a training dataset and then apply these relationships to new data. Thus, ML improves selecting and segmenting suppliers, predicting supply chain risks, inventory management, and transportation and distribution processes.

Let’s look at an example of using machine learning for supply chain forecasting. The chain of hypermarkets operates around 100 stores in different locations and has an average of 50000 SKUs per store. For such a big chain, it’s definitely required that the process of replenishment of warehouses be automated. There are two main benefits in this case:

  1. No need to store a lot of hard-to-sell products
  2. Frequently sold products should be delivered on time

Based on the previous information on replenishment of warehouses, as well as data that shows how fast certain products are selling, we can develop an ML model for predicting the number of products per SKU. The prediction could be shown with different time horizons (e.g. daily, weekly, monthly, etc.). This can help managers properly organize the system of storing products and minimize the case of product absence.


Price prediction algorithms determine how much the product must cost to be appealing to consumers, meet the company’s expectations, and assure the highest level of sales. The construction of price forecasts should take into account such factors as product features, demand, and existing trends. This approach may be perceived skeptically, yet it’s beneficial when companies enter a new market or release a new product and want to easily cope with a myriad of fluctuating factors.

Often business owners want to have an understanding of price changes for a specific product for a future period of time. Having taken into consideration client data with related price changes for a past period of time for all of the existing products, we can catch general patterns from the previous data and extrapolate them for the next periods. The positive impact could also be applied by adding external third-party data that could influence prices as well, for instance: inflation rate, holidays, seasonal patterns, etc. Wrapping up all of this data, we can develop an ML forecasting model that will be able to predict price trends for specific products.


A fluctuation in demand is a cumbersome challenge that concerns the whole e-commerce industry. That’s why companies, including manufacturers, apply ML demand forecasting to predict buyers’ behavior and find out how many products to produce or order. With ML models, it’s possible to avoid excess inventory or stockout. Moreover, such an approach to demand forecasting enables understanding the target audience and competition.

Let’s say a restaurant chain business wants to plan demand in advance. It will help the business in several ways:

  • to know the number of dishes that will be sold in the restaurant in order to plan food stock in advance,
  • to understand and define an appropriate number of employees that are required to provide quality customer service
  • to come up with the proper and timely marketing campaign

In order to develop a demand forecasting model and help businesses to fulfill their goals, it will be great to start by analyzing historical data of the previous periods. One of the ways to improve the model performance could be an integration of NLP algorithms as well. For example, we can consider reviews on Google for our restaurant chain, as well as the main competitors to identify the main dishes/quality of service that customers like or do not like.


According to a TransUnion report, there is a 52.2% increase in the rate of suspected digital fraud globally between 2019 and 2021. It indicates that companies should make greater efforts in the development of anti-fraud tactics. ML algorithms can detect suspicious financial transactions by learning from past data. They are already successfully applied in e-commerce, banking, healthcare, fintech, and other areas.

For instance, a cafe chain owner wants to analyze the productivity of employees. One of the main goals is to detect hidden patterns that allow employees to cheat. Different frauds like this could lead to losing money. Based on historical data, we can develop a fraud detection model that will detect anomaly patterns and notify about them. In this case, managers can precisely analyze detected anomalies and identify the root cause of such deviations in the data. In the future, such cases could be prevented by the manager to keep the business safe.

Key Machine Learning Forecasting Algorithms

Let’s look at some key machine learning forecasting algorithms to better understand how ML forecasting can be applied.


ML regression models are applied to predict trends and outcomes, being capable of comprehending how variables impact each other along with the results. The dependency between variables can be both linear and nonlinear, while labeled data is required for training. After understanding the relationship of variables, regression models can predict what results will be in unseen data.

Simple and multiple linear regression and logistic regression, where a target variable has only two values, are one of the most common baseline models to predict sales, stock prices, and customer behavior.


Time series forecasting implementation is gradually replenishing with new deep learning algorithms. The more versatile and explainable a model is, the higher the chances for its production use. Let’s take a look at a few deep learning models for time series forecasting.

The first one is DeepAR. It’s a supervised ML algorithm created by Amazon and based on recurrent neural networks. It has proven its efficiency with datasets consisting of hundreds of interrelated time series. The advantages of the method are the possibility to use a rich set of inputs, scaling capabilities, and suitability for probabilistic forecasting.

The second one is the Temporal Fusion Transformer (TFT). It overcomes other deep learning models in terms of versatility and can be built on multiple time series. TFT performs well even if trained on a small dataset, thus being suitable for demand forecasting as just one example.

The third algorithm is long short-term memory (LSTM) based upon an artificial RNN, in which the output from one step is transformed into the input of the next step. As for the architecture of LSTM, it consists of neural networks and memory cells for maintaining data, while any manipulation within the memory is performed by gates. There are three gates here: Forget, Input, and Output. However, LSTM requires plenty of resources and a long time for training.


Tree-based algorithms refer to supervised learning approaches. Their advantages include accuracy, sustainability, and suitability for mapping non-linear patterns. The idea here is to define homogeneous sets in the sample taking into account the key differentiator in input. The classification of tree-based algorithms depends on the target variable. As for advantages, tree-based algorithms can be easily grasped, require minimal data cleaning, and handle different types of variables. The tendency toward overfitting and irreconcilability with continuous variables may be seen as disadvantages in this case.


Gaussian processes (GP) are inferior in popularity to other models, yet they are powerful enough for industrial application, including automatic forecasting. Gaussian processes enable us to incorporate expert opinion via kernel, though their application in forecasting depends on the number of parameters and may be expensive.


The group of auto-regression algorithms foresees predicting future values using the output from the previous step as an input. Forecasting algorithms of this group include ARIMA, SARIMA, and others. In ARIMA, forecasting is carried out with the application of moving and autoregressive averages. For instance, the ARIMA model can predict fuel costs or forecast a company’s revenue based on past periods. SARIMA uses the same basic idea, but it includes a seasonal component that may affect the outcomes.


Exponential smoothing is an alternative to ARIMA models. It can be applied as a forecasting model for univariate data that can be extended to support data with a systematic trend or seasonal component. In this model, forecasting is a weighted sum of past observations, yet the importance (weight) of past observations is exponentially decreased. The accuracy of prediction depends on the type of the exponential smoothing model which can be single, double, or triple. The most sophisticated exponential smoothing models take into account trends and seasonality.

How to Apply Machine Learning Forecasting

Regardless of the chosen model, the whole adoption of ML practices looks as the following:

  1. Define business goals and available internal data
  2. Search for external data, namely market reports, trends, GDPs, product reviews, etc.
  3. Structure, clean, and label data (if needed)
  4. Identify the batch of problems to be solved with the help of forecasting
  5. Select a baseline model (usually simple regression or tree-based models) to be used as a first reference point to start with
  6. Improve models’ performance by implementing more sophisticated ML models or adjusting the data
  7. After achieving comfortable results, the model is implemented into production (added to existing software and used on more data)

Challenges of ML Forecasting

Nothing good comes without challenges, ML forecasting is no exception. Key business forecasting with machine learning challenges include the following:

  • Insufficient amount of data to train a model
  • An incorrectly chosen metric to evaluate results in alignment with business needs
  • Imputation of missing data
  • Dealing with outliers/anomalies

While infusing the data at the scale of AI, businesses encounter difficulties and limitations, that’s why it’s crucial to involve experienced data science professionals and AI engineers when implementing machine learning.

Top comments (2)

leonardpuettmann profile image
Leonard Püttmann

Awesome article Anastasiia! Learned a lot! :-)

anastasiiamolodoria profile image
Anastasiia Molodoria

Thank you, Leonard!