In most existing studies on trust evaluation, a single trust value is aggregated from the ratings given to previous services of a service provider, to indicate his/her current trust level. Such a mechanism is useful but may not be able to depict the trust features of a service provider well under certain circumstances. Alternatively, a complete set of trust ratings can be transferred to a service client for local trust evaluation. However, this incurs a big overhead in communication, since the rating dataset is usually in large scale covering a long service history. The third option is to generate a small set of data that should represent well the large set of trust ratings of a long time period. In the literature, a trust vector approach has been proposed, with which a trust vector of three values resulting from a computed regression line can represent a set of ratings distributed within a time interval (e. g., a week or a month, etc.). However, the computed trust vector can represent the set of ratings well only if these ratings imply consistent trust trend changes and are all very close to the obtained regression line. In a more general case with trust ratings for a long service history, multiple time intervals have to be determined, within each of which a trust vector can be obtained and can represent well all the corresponding ratings. Hence, a small set of data can represent well a large set of trust ratings with well preserved trust features. This is significant for large-scale trust rating transmission, trust evaluation and trust management. In this paper, we propose one greedy and two optimal multiple time interval (MTI) analysis algorithms. We also have studied the properties of our proposed algorithms analytically and empirically. These studies can illustrate that our algorithms can return a small set of MTI to represent a large set of trust ratings and preserve well the trust features.