Time Series Analysis There are two types of analysis schemes, namely the qualitative analysis technique and the quantitative mode of analysis technique. Each of them has its own strengths and weaknesses, and they have a vast field under them. Time series analysis is an element of the same process and belongs to the quantitative mode of analysis…
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The measurement is based on a proper sequence, and time intervals are equally distant and uniform (Schelter, Winterhalder et al. 319). The main aim of this kind of analysis is to determine any possible existence of a pattern or sequence in a given set of data. The time series analysis itself offers variety of methods, namely the forecasting approach, the univariate approach, which involves limited variables, and other advanced techniques like Gaussian and Box-Jenkins model. Large number of events can be counted as examples of time series analysis that we see in our daily life in our routine activities. For example, the constant rise in the inflation rate, the unemployment rate, the rise in salary, local currency depreciation, annual budgets estimation and comparison with the past values and prediction of upcoming budgets – all these things are possible through the effective tool known as time series analysis. Time series analysis is a vast entity in itself and contains various other methods and approaches, which makes it one of the most effective means of quantitative analysis of data. Various types of Time Series Analysis Continuous time series As the name applies, the samples and patterns are collected over a continuous and recurrent time frame (Tsay 287). Discrete In contrast to continuous time series, the discrete method attains certain values at fixed and definitive moments. Deterministic vs. Stochastic The data so obtained is deterministic in nature, that is, the accuracy and predication level is relatively high and accurate. The stochastic method involves relative use of probability and assumption based on the trends. These trends are collected from the past and present values, which enables the prediction of future trends. Advantages There are a number of advantages attached to this form of analysis; the first and foremost is the possibility to analyze things based on solid foundations and evidence, which involves study and consideration of samples and patterns from past values and may include the data from present values if a future trend is to be determined. It enables gathering data on a more consistent pattern that is relatively more reliable. Another advantage of this pattern is the co-relational factor and dependency between the variables involved. With the element of dependency in the analysis, the results become more reliable and consistent, and in such cases a change in one, or any other alteration, might result in disturbance and variation in the other, so the entire system is under a uniform control and each entity is dependent on the presence and behavior of the other entity in the system under analysis. Due to this feature, it has the ability to determine the linear and non-linear functions and relations. Other salient features of time series analysis include constant observation, with no data missing in-between, and the time slots and observational chunks are equally spaced. Applications Though time series analysis finds its application in a large number of places and circumstances, the most notable of them is the process of forecasting. Forecasting is an essential tool of managerial world and in other processes where predictions are needed and made about a certain future value. Time series analysis is the best tool for it. The process is naturally designed in such a way that completely fulfils the requirements of a
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(“Quantitative analysis Essay Example | Topics and Well Written Essays - 2250 words”, n.d.)
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(Quantitative Analysis Essay Example | Topics and Well Written Essays - 2250 Words)
“Quantitative Analysis Essay Example | Topics and Well Written Essays - 2250 Words”, n.d. https://studentshare.org/other/1400749-quantitative-analysis.
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