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PERFORMANCE COMPARISON OF TIME SERIES DATA USING PREDICTIVE DATA MINING TECHNIQUES

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Author(s): SAIGAL S. | MEHROTRA D.

Journal: Advances in Information Mining
ISSN 0975-3265

Volume: 4;
Issue: 1;
Start page: 57;
Date: 2012;
Original page

Keywords: Exchange Rate Prediction | Time Series Models | Regression | Predictive Data Mining | Weka | VAR | Neural Network.

ABSTRACT
This paper focuses on the methodology used in applying the Time Series Data Mining techniques to financial time series data for calculating currency exchange rates of US dollars to Indian Rupees. Four Models namely Multiple Regression in Excel, Multiple Linear Regression of Dedicated Time Series Analysis in Weka, Vector Autoregressive Model in R and Neural Network Model using NeuralWorks Predict are analyzed. All the models are compared on the basis of the forecasting errors generated by them. Mean Error (ME), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE) are used as a forecast accuracy measure. Results show that all the models accurately predict the exchange rates, but Multiple Linear Regression of Dedicated Time Series Analysis in Weka outperforms the other three models.

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