To predict the future events the regularities, obtained in the data tables, describing the past events, are applied. Each string ai from m strings of such table contains n characteristics xj (j=1,2,…,n) of the observed object or process state at i-th moment of time. The strings are sorted according to the «age»: the most fresh data has an index i=1, the preceding data – i=2 and so on until the moment i=m. The nearest time moment to be predicted has an index i=0. Let us give a few examples of «time-property» type tables and prognosis problems, solved on these tables.       1. The table represents the results of daily trading at a stock exchange. The xj column here corresponds to the cost of j-th company stocks. The string ai reveals the cost of all companies stocks at i-th day. If it will be possible to find the regularities in stock prices data during last m days, then it will be possible predict the stocks prices for one or more days ahead.       2. The table contains the information about the value of weather characteristics for 10-days periods during many years. The regularities of weather conditions alternation, if they will be found, will give the way to predict the weather for one or few 10-days periods ahead.      3. The table contains the data about the consumption of electricity during m last weeks. It has 7 columns, corresponding to the week days. Each i-th string reveals the electricity consumption during i-th week. It is required to find the regularity between electricity consumption and time of the year or the week day to predict the loading of power circuit for each day of the next week or for few weeks ahead.       ZET-D and GAP programs are included into OTEKS software for solution of such problems.