Time series analysis has two important aims

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• Quantitative Methods
•  
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• Time series analysis has two important
  aims 1) recognizing the quality of the
  phenomenon shown by the series of studies, and 2)
  Both the aims need the plan of the viewed time
  series data is recognized and somewhat officially
  explained A time series is said to be a
  'collection of observations made in sequence with
  time'. For example recording level of daily
  rainfall, periodical total domestic product of
  US, and monthly strength of the. workers in
  Marine Corps for a specific rank and MOS. The
  evaluation of time series gives instruments for
  picking a symbolic model and delivering
  forecasts. There are two sorts of times series
  data Continuous in this the data consists of
  study at every moment, for example, seismic
  movement recorded on a seismogram. Discrete
  the data contains recordings taken at different
  periods ,like, statistics of each month crime.

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• Until the data is absolutely haphazard,
  studies in time series are usually related to
  each and the following studies could be partly
  ascertain by the last values. For instance, the
  reasons pertaining to the meteorology which have
  an effect on the temperature for any given day
  tend to have some affect on the next day's
  climate. Hence, the observations of the past
  temperature are helpful for predicting
  temperatures for the following days. A time
  series can be deterministic if there are no
  haphazard or feasible features but goes in a set
  and foreseeable manner. The data gathered during
  the classical physics experiment like showing
  Newton's Law of Motion, is one example of a
  deterministic time series. The stochastic type of
  series is more appropriate to the econometric
  function. Stochastic variables contain undefined
  or arbitrary viewpoint. Though the worth of each
  study cannot be precisely foreseen, calculating
  the various observations could follow the
  expected method. These methods can be explained
  through the statistical models.

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• According to these models, studies differ
  erratically on the underlying mean value which is
  the role of time. Time series data can be put in
  the following categories one or more performance
  factors trend, seasonality, cyclical function
  and random sound. Various kinds of time series
  predicting models give forecasts through
  extrapolating the previous performance of the
  values of a specified \'l!riable of interest.
  Consecutive study in econometric times series are
  generally not free and forecast can be made on
  the basis of last observations. Although precise
  predictions can be made with deterministic time
  series, predictions of stochastic time series are
  restricted to 'conditional statements regarding
  the future on the basis of particular
  hypothesis.' Armstrong (2001) says, "The basic
  Assumption is that the variable ui!! continue in
  the future as it has behaved in the past. "
  Particularly, the time series predictions are
  suitable for stochastic type of data in which the
  fundamental root cause of variation like, trend,
  cyclical performance, seasonality, and uneven
  variations, do not change radically m time.
  Therefore, modeling is considered to be more
  suitable temporarily instead of permanent
  predictions.

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• Answer the following question.
•  
• Q1.Write briefly on time-series analysis.
  (Hint recognizing the quality of the phenomenon
  shown by the series of studies, and, both the
  aims need the plan of the viewed time series data
  is recognized and somewhat officially explained)
•  

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