Forecasting Tasks

Medical Diagnostic Tasks

The objects are patients. Signs characterize the results of examinations, symptoms of the disease and the methods of treatment used. Examples of binary signs: gender, headache, weakness. An ordinal sign is the severity of the condition (satisfactory, moderate, severe, extremely severe). Quantitative signs - age, pulse, blood pressure, hemoglobin in the blood, dose of the drug. The characteristic description of the patient is, in fact, a formalized medical history . Having accumulated a sufficient number of precedents in electronic form, it is possible to solve various problems:

• classify the type of disease ( differential diagnosis );
• determine the most appropriate method of treatment;
• predict the duration and outcome of the disease;
• assess the risk of complications;
• to find syndromes - the most characteristic set of symptoms for a given disease.

The value of such systems is that they are able to instantly analyze and generalize a huge number of precedents - an opportunity not available to a specialist doctor.

Mineral Prediction

The signs are geological exploration data. The presence or absence of certain breeds in the district is encoded by binary signs. The physicochemical properties of these rocks can be described both quantitatively and qualitatively. The training sample is made up of precedents of two classes: areas of known deposits and similar areas in which the mineral of interest was not found. When searching for rare minerals, the number of objects can be much less than the number of signs. In this situation, classical statistical methods do not work well. The problem is solved by searching for patterns in the existing data array. In the process of solving, short sets of features are selected that have the most informativeness - the ability to best separate classes. By analogy with the medical task, we can say that “syndromes” of deposits are being sought. This is an important by-product of the study, of considerable interest to geophysicists and geologists.

Credit rating of borrowers

This problem is solved by banks when issuing loans. The need to automate the process of issuing loans first arose during the boom of credit cards in the 1960s and 1970s. in the USA and other developed countries. The objects in this case are individuals or legal entities applying for a loan. In the case of individuals, an indicative description consists of a questionnaire that the borrower fills out, and possibly additional information that the bank collects about it from its own sources. Examples of binary signs: gender, telephone availability. Nominal signs - place of residence, profession, employer. Ordinal signs - education, position held. Quantitative signs - loan amount, age, work experience, family income, amount of debts in other banks. The training sample is made up of borrowers with a known credit history. In the simplest case, decision-making boils down to classifying borrowers into two classes: “good” and “bad”. Loans are granted only to first-class borrowers. In a more complex case, the total number of points (score (eng.) ) Of the borrower scored by the totality of informative features is estimated. The higher the rating, the more reliable the borrower is considered. Hence the name - credit scoring . At the training stage, the synthesis and selection of informative features is carried out and it is determined how many points to assign for each feature so that the risk of decisions being made is minimal. The next task is to decide on what conditions to grant a loan: determine the interest rate, maturity, and other parameters of the loan agreement. This problem can also be solved by teaching methods on precedents.

Consumer Demand Forecasting

It is solved by modern supermarkets and retail retail chains. For effective management of the distribution network, it is necessary to predict the sales volumes for each product for a given number of days in advance. Based on these forecasts, procurement planning, assortment management, pricing policy development, and planning of promotions (advertising campaigns) are carried out. The specifics of the problem is that the number of goods can be in the tens or even hundreds of thousands. Predicting and making decisions on each product “manually” is simply unthinkable. The initial data for forecasting are time series of prices and sales volumes for goods and for individual stores. Modern technology allows you to remove this data directly from cash registers. To increase the accuracy of forecasts, it is also necessary to take into account various external factors affecting consumer demand: inflation , weather conditions, advertising campaigns, socio-demographic conditions, and the activity of competitors. Depending on the objectives of the analysis, the objects are either goods, or stores, or “store, product” pairs. Another feature of the problem is the asymmetry of the loss function. If the forecast is made for the purpose of procurement planning, then the losses from the underestimated forecast are significantly higher than the losses from the overestimated one.

Making investment decisions in the financial market

In this task, the ability to forecast well turns into profit in the most direct way. If the investor assumes that the share price rises, he buys the shares, hoping to sell them later at a higher price. Conversely, predicting a fall in prices, the investor sells shares in order to subsequently buy them back at a lower price. The task of the investor-speculator is to correctly predict the direction of future price changes - growth or fall. Automatic trading strategies are very popular - algorithms that make trading decisions without human intervention. The development of such an algorithm is also a task of training with a teacher. The objects are situations, in fact, time instants. Description of the object - this is the whole history of changes in prices and trading volumes, fixed to this moment. In the simplest case, objects must be classified into three classes that correspond to possible solutions: buy, sell or wait. The training sample for setting up trading strategies is historical data on the movement of prices and volumes over a certain period of time. The quality criterion in this problem differs significantly from the standard functional of the average error, since the investor is not interested in the accuracy of forecasting, but in maximizing the total profit. Modern stock market technical analysis has hundreds of parametric trading strategies, the parameters of which are customary to adjust according to the criterion of maximum profit in the selected history interval.

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