Not sure what you mean by this. TimescaleDB outperforms InfluxDB, another purpose-built time-series DB, on most workloads [0], especially on ones with high-cardinality [1].
In particular our key insight, which some may still find heretical and hard-to-believe, is that it is quite possible to produce best-in-class performance characteristics for time-series using a relational database. In particular, we have been able to add columnar compression to our row-oriented format resulting in 96%+ compression rates [2], and multi-node scale-out resulting in 10M+ inserts per second [3].
TimescaleDB is not designed for workloads where most queries touch all data points (ie full table scans). But that's not what time-series workloads look like.
[0] https://blog.timescale.com/blog/timescaledb-vs-influxdb-for-...
[1] https://blog.timescale.com/blog/what-is-high-cardinality-how...
[2] https://blog.timescale.com/blog/building-columnar-compressio...
[3] https://blog.timescale.com/blog/building-a-distributed-time-...
Time series DBs (and OLAP dbs in general) have very different trade-offs/needs than transactional DBs.