Similarity search in time series data mining is a problem that has attracted increasing attention recently. The high dimensionality and large volume of time series databases make sequential scanning inefficient to tackle this problem. There are many representation techniques that aim at reducing the dimensionality of these time series so that the search can be handled faster at a lower dimensional space level. Symbolic representation is one of the promising techniques, since these symbolic methods try to benefit from the wealth of search algorithms used in bioinformatics and text mining communities. The symbolic aggregate approximation (SAX) is one of the most competitive methods in the literature. SAX utilizes a similarity measure, which is easy to compute because it is based on pre-computed distances obtained from lookup tables. In this paper we present a new similarity distance that is almost as easy to compute as the original similarity distance, but it is tighter than the original similarity distance because it uses updated lookup tables. In addition, the new similarity distance is more intuitive. We conduct several experiments which show that this new similarity distance gives better results than the original similarity distance