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Time series forecasting, especially long-term prediction, is a challenge in many fields of science and engineering. Many techniques exist for time series forecasting. In general, the object of these techniques is to build a model of the process and then use this model on the last values of the time series to extrapolate past behavior into future. Forecasting procedures include different techniques and models. Although the search for a best time series forecasting method continues, it is agreeable that no single method will outperform all others in all situations. New developments and trends in the broad area of time series prediction based on fuzzy and neural networks will be reviewed in this talk. A new method for the identification of an optimal set of time lags based on non-uniform attractor embedding from the observed non-linear time series is discussed. Simple deterministic method for the determination of non-uniform time lags comprises the pre-processing stage of the time series forecasting algorithm which is implemented in the form of a fuzzy inference system. Experiments done with benchmark chaotic time series show that the proposed method can considerably improve the forecasting accuracy. The proposed method is an efficient candidate for prediction of time series with multiple time scales and noise [1]. A near-optimal set of time lags is identified by evolutionary algorithms. The fitness function is constructed in such a way that it represents the spreading of the attractor in the delay coordinate space but does not contain any information on prediction error metrics. The weighted one-point crossover rule enables an effective identification of near-optimal sets of non-uniform time lags which are better than the globally optimal set of uniform time lags. Thus the reconstructed information on the properties of the underlying dynamical system is directly elaborated in the fuzzy prediction system. A number of numerical experiments are used to test the functionality of this method [2]. A new short-term time series forecasting method based on the identification of skeleton algebraic sequences is discussed in this talk. The concept of the rank of the Hankel matrix is exploited to detect a base fragment of the time series. Particle swarm optimization and evolutionary algorithms are then used to remove the noise and identify the skeleton algebraic sequence. Numerical experiments with an artificially generated and a real-world time series are used to illustrate the functionality of the proposed method [3].