  Pattern similarity-based methods are widely used in classification and
regression problems. Repeated, similar-shaped cycles observed in seasonal time
series encourage to apply these methods for forecasting. In this paper we use
the pattern similarity-based methods for forecasting monthly electricity demand
expressing annual seasonality. An integral part of the models is the time
series representation using patterns of time series sequences. Pattern
representation ensures the input and output data unification through trend
filtering and variance equalization. Consequently, pattern representation
simplifies the forecasting problem and allows us to use models based on pattern
similarity. We consider four such models: nearest neighbor model, fuzzy
neighborhood model, kernel regression model and general regression neural
network. A regression function is constructed by aggregation output patterns
with weights dependent on the similarity between input patterns. The advantages
of the proposed models are: clear principle of operation, small number of
parameters to adjust, fast optimization procedure, good generalization ability,
working on the newest data without retraining, robustness to missing input
variables, and generating a vector as an output. In the experimental part of
the work the proposed models were used to forecasting the monthly demand for 35
European countries. The model performances were compared with the performances
of the classical models such as ARIMA and exponential smoothing as well as
state-of-the-art models such as multilayer perceptron, neuro-fuzzy system and
long short-term memory model. The results show high performance of the proposed
models which outperform the comparative models in accuracy, simplicity and ease
of optimization.
