Abstract
A discrete- time signal or time series is set of observations taken sequentially in time, space, or some other independent variable. Examples occur in various areas including engineering, natural sciences, economics, social sciences and medicine. Financial time series in particular are very difficult to model and predict, because of their inherent nature. Hence, it becomes essential to study the properties of signal and to develop quantitative techniques. The key characteristics of a time series are that the observations are ordered in time and that adjacent observations are related or dependent. In this paper a case study has been performed on the BSE and NSE index data and methods to classify the signals as Deterministic, Random or Stochastic and White Noise are explored. This pre-analysis of the signal forms the basis for further modeling and prediction of the time series.
Keywords
- Time Series
- Signal Analysis
- Time Series Analysis
- Deterministic
- Stochastic.
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Sivakumar, P.B., Mohandas, V. (2007). Evaluating the Predictability of Financial Time Series. In: Sobh, T. (eds) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6268-1_19
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DOI: https://doi.org/10.1007/978-1-4020-6268-1_19
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6267-4
Online ISBN: 978-1-4020-6268-1
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