Univariate Time Series Forecasting Using k-Nearest Neighbors Algorithm: A Case for GDP
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k-Nearest Neighbors (k-NN) is a well know algorithm used for classification and regression. Its usage in time series forecasting is limited though, despite its simplicity and competitive accuracy, as it has been demonstrated in relevant research. This work presents a method for time series forecasting based on k Nearest Neighbors regression, which can be utilized for macroeconomic variable forecasting, like Gross Domestic Product. The approach focuses on one-step ahead forecast, and uses R package libraries for the implementation. The method is applied to forecast Greek Gross Domestic Product and the accuracy results are high and comparable to ARIMA approach. The work offers a competent approach for time series and GDP forecasting, which is comparable in accuracy to traditional statistical approaches, and can be further developed experimentation on diverse data sets can improve parameter tuning and aggregation approach.
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