In the dynamic world of business analytics, Daulet Yermanov emerges as a trailblazer in sales forecasting innovation. A visionary in machine learning and data science, Yermanov has developed cutting-edge solutions that are reshaping how companies predict their sales. His profound expertise in artificial intelligence applications has positioned him at the forefront of a technological revolution in business forecasting.
Yermanov’s approach leverages multiple ensemble learning models to completely transform forecasting methods. While most companies still rely on single models or simple algorithm combinations, Yermanov has developed a system that applies several ensemble learning models and then dynamically selects the best-performing ensemble for each individual product. This dual-layered approach results in unprecedented forecast accuracy, even in the most complex and dynamic market conditions.
“In today’s volatile market, companies that don’t use advanced forecasting methods risk losing their competitive edge,” Yermanov notes. He emphasizes that accurate forecasts are crucial for optimizing inventory, planning production, and allocating resources efficiently. This is particularly true in industries with complex supply chains or rapidly changing consumer preferences.
Yermanov’s approach, rooted in ensemble learning, promises to overcome the limitations of traditional forecasting methods. “This isn’t just an improvement on existing processes,” he explains. “It’s a genuine breakthrough in predictive analytics.” The potential implications of this breakthrough are far-reaching, potentially transforming how businesses approach everything from supply chain management to marketing strategies.
According to Yermanov, conventional forecasting techniques like linear regression can’t handle the complexities of modern markets. “These methods struggle with non-linear relationships and can’t adapt quickly to rapid market changes,” he points out. “They simply aren’t designed to process vast amounts of data or account for the many factors influencing today’s sales patterns.” This limitation has become increasingly problematic as businesses generate and collect more data than ever before.
“His solution? A combination of ensemble learning models. ‘It’s not just another algorithm,’ Yermanov explains. ‘It’s a whole new philosophy of predictive modeling.’ His system applies multiple ensemble learning algorithms, such as XGBoost and Random Forest, which internally combine multiple models to improve accuracy. From these ensemble models, Yermanov’s system selects the best one for each SKU, offering a level of nuance and adaptability that single-model systems simply can’t match.
Think of it as consulting a panel of expert ensembles rather than relying on a single method,” Yermanov explains. “Each ensemble learning model captures different patterns and nuances in the data. By selecting the best ensemble for each SKU, we ensure the most accurate forecast possible, leveraging the strengths of multiple algorithms in a tailored approach.
What sets Yermanov’s approach apart from existing industry practices is its dynamic, SKU-specific model selection and combination. While many companies use ensemble methods, they typically apply a fixed set of models across all products. Yermanov’s innovation lies in his system’s ability to autonomously select and weigh the most effective models for each individual SKU, adapting to the unique characteristics and patterns of each product. This level of granularity and adaptability is unprecedented in the industry. Furthermore, Yermanov’s approach incorporates real-time market data and uses advanced machine learning techniques to continuously refine and update its model selections, ensuring that the forecasts remain accurate even in rapidly changing market conditions. This combination of SKU-level customization, real-time adaptation, and autonomous model selection pushes the boundaries of what’s currently possible in sales forecasting.
Yermanov’s approach applies several ensemble learning models, including XGBoost and Random Forest, which internally combine predictions from different algorithms. Once these ensemble models generate predictions, the system selects the best-performing one for each SKU, ensuring a forecast that is specifically tailored to the product’s unique sales dynamics. This two-step process—first leveraging ensemble learning and then selecting the best ensemble—provides an unmatched level of accuracy and adaptability.
The revolutionary nature of Yermanov’s approach is particularly evident in highly volatile industries such as fashion or electronics. Here, traditional forecasting methods often fail due to rapidly changing trends and short product lifecycles. Yermanov’s system, capable of accounting for complex interrelationships between multiple factors and quickly adapting to changes, allows companies to reduce excess inventory levels by 30-40% and increase sales forecast accuracy to 85-90%. This is not just an improvement – it’s a qualitative leap that can radically change the approach to supply chain management and production planning in these industries.
“We’re not just improving forecast accuracy,” Yermanov emphasizes. “We’re gaining deeper insights into the factors driving sales dynamics. This leads to more informed business decisions across the board.” These insights can be invaluable for businesses looking to gain a competitive edge in their respective markets.
At the heart of Yermanov’s innovation is a multi-algorithmic approach that uses ten different models for each Stock Keeping Unit (SKU). As an example, the process starts with five years of historical sales data, using 4.5 years for training and the remaining six months for accuracy verification. This extensive use of historical data helps ensure the model’s predictions are grounded in real-world patterns and trends.
The forecasting process, as Yermanov describes it, is both comprehensive and efficient. “After running all 10 algorithms, we compare their accuracy and choose the most effective one for each specific SKU,” he notes. “This process is fully automated and takes about 5 minutes per SKU.” This efficiency is crucial for businesses dealing with large numbers of SKUs, where manual forecasting would be prohibitively time-consuming.
Yermanov’s toolkit includes a carefully selected range of algorithms, each bringing unique strengths to the ensemble. Prophet, developed by Facebook, excels in analyzing time series with strong seasonal effects and non-linear trends. Auto ARIMA automatically optimizes parameters for time series forecasting, particularly effective for data with consistent patterns over time.
XGBoost, a powerful gradient boosting algorithm, captures complex relationships between various factors influencing sales. Random Forest provides high resilience to outliers and works well with categorical variables. Long Short-Term Memory (LSTM) neural networks excel at analyzing long data sequences, particularly effective when there are long-term dependencies in sales data.
The ensemble is completed with LightGBM, CatBoost, Exponential Smoothing models (ETS), DeepAR, and Decision Trees. “Each of these contributes its unique input to the overall forecast accuracy,” Yermanov explains. This diversity of algorithms is key to the system’s ability to handle a wide range of forecasting scenarios.
Two of the most popular libraries for ensemble learning are XGBoost and Random Forest, each using different methods for prediction. Random Forest uses tree-bagging, while XGBoost employs a sequential model for boosting. Although their approaches differ, both are highly effective in handling complex predictive tasks. In Yermanov’s system, these ensemble models, along with others, are compared, and the most accurate ensemble is selected for each SKU.
XGBoost has the simplest syntax among others. Which is important in terms of supporting code created for the benefit of e-commerce. Getting a prediction with XGBoost can be accomplished with six lines of code:
from xgboost import XGBClassifier
# read data
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data[‘data’], data[‘target’], test_size=.2)
# create model instance
bst = XGBClassifier(n_estimators=2, max_depth=2, learning_rate=1, objective=’binary:logistic’)
# fit model
bst.fit(X_train, y_train)
# make predictions
preds = bst.predict(X_test)
With a little development of this code you can get the functionality of price prediction on the site of an e-commerce site or online store. Experienced developers of IT solutions use a stack of libraries, customize each one depending on its strengths.
“The power isn’t in individual algorithms,” he emphasizes. “It’s in their synergy. We’re offsetting weaknesses in some algorithms with strengths in others.” This synergistic approach allows the system to adapt to different types of data and market conditions, providing robust and reliable forecasts across various scenarios.
Crucial to the success of this approach is the careful selection of factors for each SKU. “It’s both an art and a science,” Yermanov notes. Key considerations include pricing (including competitors’ prices), inventory levels, promotional activities, seasonality, and macroeconomic indicators. The ability to incorporate such a wide range of factors allows for a more nuanced and accurate forecast.
“We don’t limit ourselves to a standard set of factors,” the forecasting expert elaborates. “For each SKU, we identify unique influencing factors. This process is iterative and requires constant testing of new hypotheses.” This flexibility allows the system to adapt to changing market conditions and new sources of data as they become available.
Yermanov cautions against including too many factors, which can lead to model overfitting. “The skill lies in finding the balance between informativeness and noise,” he explains. “A deep understanding of influencing factors is what allows us to create reliable forecasts for business decision-making.” This balance is crucial for ensuring the model remains both accurate and generalizable.
Recognizing that even the most sophisticated algorithm is only as good as its user interface, Yermanov’s team has developed an intuitive web application. “We’ve made complex algorithms accessible to employees without technical knowledge,” he says. The interface features easy data uploading, automatic factor selection, powerful visualization tools, and scenario comparison capabilities. This focus on usability ensures that the power of the forecasting system can be leveraged across the organization, not just by data scientists.
“Creating a user-friendly interface is just as challenging as developing algorithms,” Yermanov admits. “The ease of use determines how actively and effectively the system will be applied in business.” This emphasis on user experience is a key factor in the system’s successful adoption across various industries.
The impact of this approach on business metrics has been substantial. Beyond improved forecast accuracy, companies have seen optimized inventory management, reduced stockouts, and significant time and resource savings. “Tasks that once took a team of six people three days can now be completed by one person in an hour,” Yermanov reports. These efficiency gains can translate into significant cost savings and improved competitiveness.
The forecasting expert notes the flexibility and adaptability of the system to various industries, its ability to quickly respond to changes in market conditions, and provide deep analytical data. “Our approach helps companies become more data-driven, making decisions based on facts rather than intuition,” Yermanov explains. He stresses that investments in advanced forecasting methods significantly increase a company’s competitiveness in the market.
As we stand on the brink of a new era in business analytics, Daulet Yermanov’s ensemble learning approach represents more than just a technological advancement—it’s a paradigm shift in how businesses can harness data to shape their future. By combining the power of multiple algorithms with unprecedented adaptability, this method promises to transform industries ranging from retail to pharmaceuticals.
The key takeaway is clear: in an increasingly complex and data-driven world, the ability to accurately forecast and rapidly adapt is not just an advantage—it’s a necessity for survival and growth. Yermanov’s innovation offers businesses the tools to navigate this complexity with confidence.
Looking ahead, we can expect to see a growing symbiosis between machine intelligence and human intuition. As Yermanov himself notes, “The future lies not in AI replacing human decision-making, but in enhancing it.” This fusion of artificial and human intelligence may well be the key to unlocking unprecedented business insights and competitive advantages in the years to come.
As companies continue to grapple with uncertainty and rapid change, those who embrace advanced forecasting methods like Yermanov’s multi-ensemble learning approach will be best positioned to thrive. The question is no longer whether these technologies will reshape business—it’s who will lead the way in harnessing the full potential of combining ensemble learning and dynamic model selection.
