Indian Science Cruiser

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Time Series Analysis: A Brief History and Its Future Challenges


Affiliations
1 Department of Mathematics, University Institute of Technology, The University of Burdwan, Golapbag (North), Burdwan-713104, India
     

   Subscribe/Renew Journal


In the present work a brief but exhaustive history of the researches on the time series analysis is demonstrated. It is also tried in the present work to furnish a detailed outline of the contemporary research status on this branch of study. Finally, comments and thoughts have been placed on the future challenges that are to be explored and resolved to continue the successful journey of researches in time series analysis.

Keywords

Time Series Analysis, History, Validation of Theory, Modeling, Future, Challenges.
User
Subscription Login to verify subscription
Notifications
Font Size

  • A Nielsen, Practical Time Series Analysis, O’Reilly Media, Inc., 2019.

  • F X Diebold, On the origin(s) and development of the term ‘Big Data’, PIER Working Paper 12-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, 2012.

  • E Siegel, Predictive analytics: The power to predict who will click, buy, lie, or die, Hoboken, New Jersey: John Wiley & Sons, 2013.

  • D Laney, 3D data management: Controlling data volume, velocity, and variety, Application Delivery Strategies, META Group, 2001.

  • I Grattan-Guinness, Chapter 26 - Joseph Fourier, Théorie analytique de la chaleur, 1822, Editor(s): I. Grattan-Guinness, R. Cooke, L. Corry, P. Crépel and N. Guicciardini, Landmark Writings in Western Mathematics 1640-1940, Elsevier Science, page 354-365, 2005.

  • S L Lauritzen, Time series analysis in 1880: A discussion of contributions made by T.N. Thiele, International Statistical Review, Vol 49, page 319 –333, 1981.

  • S L Lauritzen, Aspects of TN Thiele’s Contributions to Statistics, Bulletin of the International Statistical Institute, Vol 58, page 27–30, 1999.

  • H Jochmann, Period variations of the Chandler wobble, Journal of Geodesy, Vol 77, No 7, page 454-458, 2003.

  • J M Blackledge, Digital signal processing: mathematical and computational methods, software development and applications, Horwood, 2006.

  • R Hooker, Correlation of the Marriage-Rate with Trade (Journal of the Royal Statistical Society, Vol 64, page 485–92, 1901) In D. Hendry and M. Morgan, The Foundations of Econometric Analysis, Cambridge: Cambridge University Press, page 122129, 1995.

  • G U Yule, On a Method of Investigating Periodicities in Disturbed Series with Special Reference to Wölfer’s Sunspot Numbers, Philosophical Transactions of the Royal Society London, Ser. A, Vol 226, page 267-298, 1927.

  • G Walker, On Periodicity in Series of Related Terms, Proceedings of the Royal Society of London, Ser. A, Vol 131, page 518–532, 1931.

  • S -M Shams, G -A Hossein-Zadeh and H Soltanian-Zadeh, Multisubject activation detection in fMRI by testing correlation of data with a signal subspace, Magnetic Resonance Imaging, Vol 24, No 6, page 775-784, 2006.

  • H T Davis, The Analysis of Economic Time Series, Cowles Commission Monograph No. 6, 1941. Bloomington, IN: Principia Press; reissued San Antonio, TX: The Principia Press of Trinity University, 1963.

  • N Weiner, Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications, MIT Press, 1942.

  • P Shaman, An Approximate Inverse for the Covariance Matrix of Moving Average and Autoregressive Processes, The Annals of Statistics, Vol 3, No 2, page 532-538, 1975.

  • R Prébisch, The Economic Development of Latin America and Its Principal Problems, New York: United Nations, 1950.

  • H E Hurst, Long-term storage capacity of reservoirs, Transactions of American Society of Civil Engineers, Vol 116, page 770-799, 1951.

  • R G Brown, Exponential Smoothing for Predicting Demand, Cambridge, Arthur D. Little Inc., 15, 1956.

  • C C Holt, Forecasting Trends and Seasonal by Exponentially Weighted Averages, Office of Naval Research Memorandum, 52, 1957.

  • P R Winters, Forecasting Sales by Exponentially Weighted Moving Average, Management Science, Vol 6, No 3, page 324342, 1960.

  • S Makridakis, A Anderson, R Carbone, R Fildes, M Hibon, R Lewandowski, J Newton, E Parzen, and R L Winkler, The Accuracy of Extrapolation (time series) methods: results of a forecasting competition, Journal of Forecasting, Vol 1, page 111153, 1982.

  • R E Kalman and R S Bucy, New Results in Linear Filtering and Prediction Theory, Transactions of ASME, Journal of Basic Engineering, Vol 83D, page 95-108, 1961.

  • C W J Granger, Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica, Vol 37, No 3, page 424–438, 1969.

  • G E P Box and G M Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1970.

  • P J Harrison, and C F Stevens, Bayesian Forecasting, Journal of the Royal Statistical Society, Ser. B, Vol 38, page 205-247, 1976.

  • H L Gray, G D Kelley and D D McIntire, A New Approach to ARMA Modelling, Communications in Statistics, Part B, Vol 7, page 1-77, 1978.

  • R S Tsay, and G C Tiao, Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models, Journal of American Statistical Association, Vol 79, page 84-96, 1984.

  • S Ferraz-Mello, Estimation of Periods from Unequally Spaced Observations, Astronomical Journal, Vol 86, page 619-624, 1981.

  • N R Lomb, Least-squares frequency analysis of unequally spaced data, Astrophysics and Space Science, Vol 39, page 447-462, 1976.

  • J D Scargle, Statistical aspects of spectral analysis of unevenly spaced data, Astrophysical Journal, Vol 263, page 835-853, 1982.

  • T Bai and E W Cliver, A 154 day periodicity in the occurrence rate of proton flares, Astrophysical Journal, Vol 363, page 299309, 1990.

  • M Roy, V R Kumar, B D Kulkarni, J Sanderson, M Rhodes and M V Stappen, Denoising algorithm using wavelet transform, AIChE Journal, Vol 45, No 11, page 2461-2466, 1999.

  • M Tanner and W H Wong, The Calculation of Posterior Distributions, Journal of the American Statistical Association, Vol 82, page 528-550, 1987.

  • A E Geltand, and A E M Smith, Sampling-Based Approaches to Calculate Marginal Densities, Journal of the American Statistical Association, Vol. 85, page 398-409, 1990.

  • J -P Eckmann, S O Kamphorst and D Ruelle, Recurrence plots of dynamical systems, Europhysics Letters, Vol 4, No 9, page 973–977, 1987.

  • J P Zbilut and C L Jr Webber, Embeddings and delays as derived from quantification of recurrence plots, Physics Letters A, Vol 171, No 3, page 199–203, 1992.

  • C L Jr Webber and J P Zbilut, Dynamical assessment of physiological systems and states using recurrence plot strategies, Journal of Applied Physiology, Vol 76, No 2, page 965–973, 1994.

  • T Higuchi, Approach to an irregular time series on the basis of the fractal theory, Physica D: Nonlinear Phenomena, Vol 31, No 2, page 277-283, 1988.

  • T Higuchi, Relationship between the fractal dimension and the power law index for a time series: A numerical investigation, Physica D: Nonlinear Phenomena, Vol 46, No 2, page 254-264, 1990.

  • N E Huang, Z Shen, S R Long, M C Wu, H H Shih, Q Zheng, N -C Yen, C C Tung and H H Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of Royal Society London A, Vol 454, page 903-995, 1998.

  • N Scafetta and P Grigolini, Scaling detection in time series: Diffusion entropy analysis, Physical Review E, Vol 66, No 3, page 036130, 2002.

  • T C Jo, The effect of virtual term generation on the neural based approaches to time series prediction, In Proceedings of the 4th International Conference on Control and Automation (ICCA’03), Montreal, Canada, Vol 3, page 516-520, 2003.

  • L Cao, Support vector machines experts for time series 3 forecasting, Neurocomputing, Vol. 51, page 321-329, 2003.

  • T Gautama, D P Mandic, and M M Van Hulle, The delay vector variance method for detecting determinism and nonlinearity in time series, Physica D, Vol 190, page 167-176, 2004.

  • I Ahmed, Detection of nonlinearity and stochastic nature in time series by delay vector variance method, International Journal of Engineering & Technology, Vol 10, No 2, page 22–27, 2010.

  • G A Gottwald and I Melbourne, A new test for chaos in deterministic systems, Proceedings of Royal Society London A, Vol 460, page 603-611, 2004.

  • L Telesca, V Lapenna and M Macchiato, Multifractal fluctuations in earthquake-related geoelectrical signals, New Journal of Physics, Vol 7, No 1, page 214, 2005.

  • W -C Wang, K-W Chan, C -T Cheng and L Qin, A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, Journal of Hydrology, Vol 374, No 3-4, page 294–306, 2009.

  • R S Tsay, Time Series and Forecasting: Brief History and Future Research, Journal of the American Statistical Association, Vol 95, No 450, page 638-643, 2000.


Abstract Views: 401

PDF Views: 0




  • Time Series Analysis: A Brief History and Its Future Challenges

Abstract Views: 401  |  PDF Views: 0

Authors

Koushik Ghosh
Department of Mathematics, University Institute of Technology, The University of Burdwan, Golapbag (North), Burdwan-713104, India

Abstract


In the present work a brief but exhaustive history of the researches on the time series analysis is demonstrated. It is also tried in the present work to furnish a detailed outline of the contemporary research status on this branch of study. Finally, comments and thoughts have been placed on the future challenges that are to be explored and resolved to continue the successful journey of researches in time series analysis.

Keywords


Time Series Analysis, History, Validation of Theory, Modeling, Future, Challenges.

References





DOI: https://doi.org/10.24906/isc%2F2020%2Fv34%2Fi5%2F206994