  K-nearest neighbors algorithm is one of the prominent techniques used in
classification and regression. Despite its simplicity, the k-nearest neighbors
has been successfully applied in time series forecasting. However, the
selection of the number of neighbors and feature selection is a daunting task.
In this paper, we introduce two methodologies to forecasting time series that
we refer to as Classical Parameters Tuning in Weighted Nearest Neighbors and
Fast Parameters Tuning in Weighted Nearest Neighbors. The first approach uses
classical parameters tuning that compares the most recent subsequence with
every possible subsequence from the past of the same length. The second
approach reduces the neighbors' search set, which leads to significantly
reduced grid size and hence a lower computational time. To tune the models'
parameters, both methods implement an approach inspired by cross-validation for
weighted nearest neighbors. We evaluate the forecasting performance and
accuracy of our models. Then, we compare them to some classical approaches,
especially, Seasonal Autoregressive Integrated Moving Average, Holt-Winters and
Exponential Smoothing State Space Model. Real data examples on retail and food
services sales in the USA and milk production in the UK are analyzed to
demonstrate the application and the efficiency of the proposed approaches.
