Abstract: Recently,with the rise of data mining and machine learning,the research about time series analysis has become more and more abundant.As a classic method of machine learning,KNN(K-Nearest Neighbor)is widely used in various fields of time series analysis due to its simplicity and high prediction accuracy.However,the original KNN algorithm has some limitations in predicting time series.The prediction effect of directly using Euclidean distance as a measure of similarity is not ideal,and it cannot adapt to the prediction of time series with overall trends.This paper proposes an improved KNN algorithm named TSTF-KNN(Time Series Trend Fitting KNN).It improves the effect of KNN similarity measurement by normalizing the feature sequence at each moment,so that it can search for similar feature sequences more effectively.In addition,this paper adds error terms to the prediction result to adjust the prediction result so that it can predict the result more effectively.In order to verify the effectiveness of the method,this paper selects 4 public data sets from the kaggle public data sets,and preprocesses the 4 data sets to obtain 5 time series for the experiment.Then,this paper uses TSTF-KNN,KNN,single-layer LSTM(Long Short-Term Memory) neural network and ANN(Artificial Neural Network) to perform prediction experiments on 5 processed time series,analyze the prediction results,and compare the mean square error(MSE),which verifies the effectiveness of this method.Experimental results show that this method can effectively improve the accuracy and the stability of the KNN regression method for time series prediction,so that it can better adapt to the prediction scenarios of time series with overall trend changes.

Key words: Error terms, KNN, Prediction, Similarity measure, Time series analysis