Keywords Forecasting Fuzzy time series Hedge algebras Enrollments, Intervals AITEX Index fuzziness intervals semantically quantifying mapping. The experimental results, forecasting enrollments at the University of Alabama and forecasting TAIEX Index, demonstrate that the proposed method significantly outperforms the published ones about accurate level, the ease and friendliness on computing. The first one predicts the time between failures (TBFs) of. After all, the results are not accurate A new approach is proposed through a semantic-based algorithm, focus on two key steps: partitioning the universe of discourse of time series into a collection of intervals and mining fuzzy relationships from fuzzy time series, that outperforms accuracy and friendliness in computing. In this paper, two fuzzy time series based software reliability models have been proposed. sec - seconds: Supported value types: float, int Returns: 0 - if difference between item timestamp value and Zabbix server timestamp is over T seconds 1 - otherwise. That means the result does not suitable the context of the problem. Mining Fuzzy Sequential Patterns with Fuzzy Time-Intervals in Quantitative Sequence Databases 1. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. fuzzytime (sec) Checking how much an item timestamp value differs from the Zabbix server time. If the latter semantics is not paid attention, despite the computation accomplished high level of exactly but it has been distorted about semantics. However, computation in the linguistic environment one term has two parallel semantics, one represented by fuzzy sets (computation-semantics) it human-imposed and the rest (context-semantic) is due to the context of the problem. During the recent years, many different methods of using fuzzy time series for forecasting have been published.
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