Small-Scale Demographic Sequences Projection Based on Time Series Clustering and LSTM-RNN

Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for demographic sequences. In this paper, we presented the application of a hybrid model that integrates Long-Short Term Memory-Recurrent Neural Network (LSTM-RNN), time series analysis and clustering techniques where time series analysis and clustering methods provide augmentation of sequences data for the training of RNN. Comprehensive characteristics of nations from UN database are used as input to the hybrid model to predict the nations future population. The results prove that, RNN combined with time series and clustering methods has outperformed mere RNN approach without time series and clustering analysis. In addition, the hybrid Time Series and Clustering-RNN with relevant inputs lead to 20% higher predictive accuracy, measured by Root-Mean-Squared Error, compared to results produced by RNN alone.

[1]  W. D. Ray.  Spatial Time Series , 2008, Encyclopedia of GIS.

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[3]  J. Sachs,et al.  Geography, demography, and economic growth in Africa. , 1998, Brookings papers on economic activity.

[4]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[5]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[6]  Young-Seuk Park,et al.  Use of an Artificial Neural Network to Predict Population Dynamics of the Forest–Pest Pine Needle Gall Midge (Diptera: Cecidomyiida) , 2000 .

[7]  Allen C. Kelley,et al.  Aggregate Population and Economic Growth Correlations: The Role of the Components of Demographic Change , 1995 .

[8]  A. Gamble.  Crisis Without End?: The Unravelling of Western Prosperity , 2014 .

[9]  Konstantinos Kalpakis,et al.  Distance measures for effective clustering of ARIMA time-series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[10]  Ericks Rachmat Swedia,et al.  Deep Learning Long-Short Term Memory (LSTM) for Indonesian Speech Digit Recognition using LPC and MFCC Feature , 2018, 2018 Third International Conference on Informatics and Computing (ICIC).

[11]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[12]  Seyed Taghi Akhavan Niaki,et al.  Forecasting S&P 500 index using artificial neural networks and design of experiments , 2013 .

[13]  Yang Zhang,et al.  Unsupervised Feature Extraction for Time Series Clustering Using Orthogonal Wavelet Transform , 2006, Informatica.

[14]  Enric Monte,et al.  “Multiple-input multiple-output vs. single-input single-output neural network forecasting” , 2015 .

[15]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[16]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[17]  Li Wei,et al.  Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.

[18]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[19]  Sven F. Crone,et al.  Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction , 2011 .

[20]  S. Nordbotten.  Neural network imputation applied to the Norwegian 1990 population census data , 1996 .

[21]  A. T. Akinwale,et al.  Population prediction using artificial neural network , 2010 .

[22]  P. Boesiger,et al.  A new correlation‐based fuzzy logic clustering algorithm for FMRI , 1998, Magnetic resonance in medicine.

[23]  Bruce D. Spencer,et al.  Statistical demography and forecasting , 2005 .

[24]  K. Wachter,et al.  Relationships between period and cohort life expectancy: Gaps and lags , 2006, Population studies.

[25]  A. Yanmaz,et al.  Regional Frequency Analysis of Precipitation Using Time Series Clustering Approaches , 2018, Journal of Hydrologic Engineering.

[26]  Zaiyong Tang,et al.  Improving Population Estimation with Neural Network Models , 2006, ISNN.