Imputation for the analysis of missing values and prediction of time series data

Data preprocessing plays an important and critical role in the data mining process. Data preprocessing is required in order to improve the efficiency of an algorithm. This paper focuses on missing value estimation and prediction of time series data based on the historical values. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms like KNNimpute (K-Nearest Neighbours imputation), BPCA (Bayesian Principal Component Analysis) and SVDimpute (Singular Value Decomposition imputation) are not able to deal with the situation where a particular time point (column) of the data is missing entirely. This paper focuses on autoregressive-model-based missing value estimation method (ARLSimpute) which is effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Data preprocessing output is given to the input of the prediction techniques namely linear prediction and quadratic prediction. These techniques are used to predict the future values based on the historical values. The performance of the algorithm is measured by performance metrics like precision and recall. Experimental results on real-life datasets demonstrate that the proposed algorithm is effective and efficient to reveal future time series data.