Study of Forecasting on Time Series Data by Nearest Neighbor Method.

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  • 最近隣法による時系列データ予測法の検討
  • サイキンリンホウ ニ ヨル ジケイレツ データ ヨソクホウ ノ ケントウ

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Methods of forecasting on time series data by auto-regressive model, neural networks model and chaotic theorem are proposed. The methods are said to be complicated for forecasting calculations. In this paper, a simple forecasting algorithm by the nearest neighbor method is discussed. It is applied to forecasting on a monthly average temperature in Sendai city to confirm performance. The actual results indicate that the nearest neighbor method has similar performance to the auto-regressive model in forecasting on time-series data.

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