Invented by Alexander Krowitz, Martin Tapp, Kronos Technology Systems Ltd Partnershiip, Kronos Technology Systems LP

The market for methods to train machine-learning models to make simulated estimates has been rapidly growing in recent years. As businesses and organizations increasingly rely on data-driven decision-making, the need for accurate and reliable estimates has become paramount. Machine-learning models have emerged as a powerful tool to generate these estimates by analyzing large datasets and identifying patterns and trends. Training machine-learning models to make simulated estimates involves a process known as supervised learning. In this approach, the model is provided with a labeled dataset, where each data point is associated with a known outcome or estimate. The model then learns from this data to make predictions or estimates on new, unseen data. One of the key factors driving the market for methods to train machine-learning models for simulated estimates is the availability of vast amounts of data. With the proliferation of digital technologies, businesses and organizations are generating and collecting massive volumes of data. This data can be leveraged to train machine-learning models and improve the accuracy of simulated estimates. Another factor contributing to the growth of this market is the increasing complexity of business problems. Traditional analytical methods may not be sufficient to tackle these complex problems, and machine-learning models offer a more sophisticated and accurate approach. By training these models to make simulated estimates, businesses can gain valuable insights and make informed decisions. Moreover, the advancements in machine-learning algorithms and computing power have also fueled the market growth. Researchers and developers are constantly innovating and improving algorithms to enhance the performance of machine-learning models. Additionally, the availability of powerful hardware, such as GPUs (Graphics Processing Units), enables faster and more efficient training of these models. The market for methods to train machine-learning models for simulated estimates is not limited to any specific industry. It has applications across various sectors, including finance, healthcare, retail, manufacturing, and more. For example, in finance, machine-learning models can be trained to estimate stock prices or predict market trends. In healthcare, these models can be used to simulate patient outcomes or predict disease progression. Several companies and startups have emerged in this market, offering specialized tools and platforms to facilitate the training of machine-learning models for simulated estimates. These companies provide pre-built models, data preprocessing tools, and training frameworks to streamline the process for businesses. Additionally, they offer consulting services to help organizations implement and optimize these models for their specific needs. However, challenges remain in this market. One of the main challenges is the availability and quality of labeled data. Training machine-learning models requires large amounts of labeled data, which can be expensive and time-consuming to acquire. Additionally, ensuring the accuracy and reliability of the labeled data is crucial for the performance of the models. Another challenge is the interpretability of machine-learning models. As these models become more complex, it becomes difficult to understand the underlying factors and variables that contribute to the simulated estimates. This lack of interpretability can hinder the adoption of these models in certain industries where explainability is critical. In conclusion, the market for methods to train machine-learning models for simulated estimates is experiencing significant growth. The availability of vast amounts of data, advancements in algorithms and computing power, and the increasing complexity of business problems are driving the demand for these methods. However, challenges related to data availability and quality, as well as interpretability, need to be addressed to fully harness the potential of machine-learning models in making accurate and reliable simulated estimates.

The Kronos Technology Systems Ltd Partnershiip, Kronos Technology Systems LP invention works as follows

A computer-implemented training method for machine learning models is presented. The method involves collecting historical data from a database and applying one or multiple transformations on the historical data in order to create a model feature set. It then separates the model feature set into one or several pools, with each pool containing one or many model features that are homogeneous based on a common value. The method also includes creating dynamically a training data set for each pool that includes one or more model features from the pool as well as at least some historical data. For each training set, the method includes training a machine-learning model on that training set.

Background for Method for training machine-learning models to make simulated estimates

The invention relates to the use of machine learning for accurate forecasting.

This section provides a context or background. The description can include concepts that have been conceived, but not necessarily pursued. “Unless otherwise indicated, what is described here is not considered prior art for the description and claims. It is also not admitted as prior art through inclusion in this part.

Machine learning regression can be used to model numerical patterns using historical data. Machine learning regression can be based on ‘training? Machine learning regression may use?training? Beispiele to capture characteristics of their unknown probability distribution. Regression methods can dynamically create complex formulas for predicting business patterns. The training data can be viewed as examples of relationships between variables. A ‘pool’ of data can also be used to train, as machine learning algorithms can determine feature combinations dynamically. In the current environment, data can be gathered from many stores or departments that are similar to the one being predicted. The systems can detect patterns which may be uncommon in a particular store but are common in the entire organization and use the pattern detected in future predictions.

Some conventional systems use business volume forecasts in order to determine the workload required, but these systems don’t integrate multiple data sources.

Other conventional systems focus on atypical event and/or use neural net architecture for external features.

Businesses that use these systems spend resources manually correcting schedules because of the inaccuracy.

What is needed is to find a way to use machine-learning to produce accurate forecasts, without the inherent problems of the previous systems. These improved forecasts can reduce such expenditures and allow employees and organizations to focus on their core missions.

The summary below is only representative and not limiting.

The embodiments can be used to solve the above problems and realize other benefits.

While prior approaches might rely on fixed inputs and static formulas, machine learning methods can be dynamic. In some situations, features such as windowed trends or seasonality are irrelevant for predicting business volumes. They can be ignored and in other situations they are useful. A machine learning method is able to use significantly more data and features than static formulas. The training process also benefits from the expansion of data, as it combines data from multiple stores or departments to create a single “pool”. This complex model is used to predict for each unit in the prediction phase. It is also possible to easily incorporate new types of historical information into the modeling process. Data from third parties, like weather data and local events calendars, that are not part of the core business can be easily added as they become available.

In a second aspect, a method is provided that uses machine-learning regression to predict retail business volume using historical data. The method can be used to predict different types of retail business volume including sales volume and transaction volume. This method has two stages: training and prediction. In both stages, historical data is transformed into model features using multiple transformations. Data from the past may include business volume and other data types. The data may include store characteristics such as department, geographical location, weather, climate and local data. Model features can represent the exact values or transformations, such as those that capture seasonality, trends and effects of special events, like sales or store closings.

The training phase uses machine learning regression to create models that embody meaningful patterns extracted from historic data. The prediction phase uses the model on the most recent data in order to make volume predictions. Previous predictions can be used as a “backfill” if complete historical data are not available. The historical data can be used to make predictions based on the current data. The system could also have a monitoring component that can identify where system performance is lacking.

Improved forecasting can lead to better staffing decisions. Retail businesses, for example, may improve their customer experience and the efficiency and effectiveness of their operations and transaction. Correct scheduling leads to better resource use for employers, and thus reduced costs. This also leads to a better employee experience and therefore better retention.


When read with the accompanying figures, the description will make more sense.







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