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Forecasting Time-Series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with On-line Inputs Selection

Research output: Contribution in Book/Report/ProceedingsPaper


Publication date2010
Place of publicationNew York
Number of pages5
ISBN (Print)978-1-4244-6920-8
<mark>Original language</mark>English


Conference2010 IEEE World Congress on Computational Intelligence


Conference2010 IEEE World Congress on Computational Intelligence


In this paper we present results and algorithm used to predict 14 days horizon from a number of time series provided by the NN GC1 concerning transportation datasets [1]. Our approach is based on applying the well known Evolving Takagi-Sugeno (eTS) Fuzzy Systems [2-6] to self-learn from the time series. ETS are characterized by the fact that they self-learn and evolve the fuzzy rule-based system which, in fact, represents their structure from the data stream on-line and in real-time mode. That means we used all the data samples from the time series only once, at any instant in time we only used one single input vector (which consist of few data samples as described below) and we do not iterate or memorize the whole sequence. It should be emphasized that this is a huge practical advantage which, unfortunately cannot be compared directly to the other competitors in NN GC1 if only precision/error is taken as a criteria. It is also worth to require time for calculations and memory usage as well as iterations and computational complexity to be provided and compared to build a fuller picture of the advantages the proposed technique offers. Nevertheless, we offer a computationally light and easy to use approach which in addition does not require any user-or problem-specific thresholds or parameters to be specified. Additionally, this approach is flexible in terms not only of its structure (fuzzy rule based and automatic self-development), but also in terms of automatic input selection as will be described below.

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