Time series analysis has two important aims: 1) recognizing the quality of the phenomenon shown by thse serie of studies
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Quantitative Methods
Finoplastika Industries Ltd, Nigeria
(20 Marks)
Time series analysis has two important aims:
1) recognizing the quality of the phenomenon shown by thse serie 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. 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.
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.
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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Contact us at
M: 7019944355
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