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Time Series

I am going to talk about time series and time series analysis. I will study it along with its types, its models, and its applications in real life, and discover what is new.

There are many practical situations where data might be correlated. This is particularly so where repeated observations on a given system are made sequentially in time. (Reiner, 2010).

A time series is a set of statistics gathered sequentially in time. Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time. It is the systematic approach by which one goes about answering the mathematical and statistical questions posed by these time correlations.

Historically, time series methods were applied to problems in the physical and environmental sciences. This fact accounts for the basic engineering flavor permeating the language of time series analysis.

The first step in any time series investigation always involves careful scrutiny of the recorded data plotted over time.

Since the beginning of time, people have used the basics of time series analysis in order to predict and forecast events, mainly astronomical and weather phenomena. An early example is ancient Egyptians keeping records of data collected by the use of sundials and shadow clocks and using it to forecast sunrise times.

However, the first recorded and published effort that employed time series analysis was the work of J. Graunt in 1962. Although his methods were improper by mathematical standards, he is considered the pioneer of time series analysis. In his research, he compiled and analyzed the Bills of mortality (weekly mortality statistics in London) to make estimates about birth and mortality rates, and the rise and spread of certain diseases. .

A time series is a sequence of data points (indexed or listed or graphed) collected over an interval of time. Thus, it is a sequence of discrete-time data. In time series analysis, analysts record data points at intervals over a set period of time rather than just recording the data points intermittently or randomly. A time series is very frequently plotted via a run chart, which is a temporal line chart.

Time series can show how variables change over time. In other words, time is a crucial variable because it shows how the data adjusts over the course of the data points as well as the final results. It provides an additional source of information and a set order of dependencies between the data. It typically requires a large number of data points to ensure consistency and reliability, as an extensive data set ensures you have a representative sample size and that analysis can cut through noisy data. It also ensures that any trends or patterns discovered are not outliers and can account for seasonal variance.

A time series can be of two types:

Stationary: A dataset should follow the below thumb rules without having trend, seasonality, cyclical, and irregularity components of time series. The MEAN value of them should be completely constant in the data during the analysis, The VARIANCE should be constant with respect to the time-frame,The COVARIANCE, which measures the relationship between two variables, should be constant between periods of identical distance.

Non- Stationary: This is just the opposite of a stationary time series.

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. It deals with time series data, or trend analysis. It means that data is in a series of particular time periods or intervals.

A time series is a set of statistics, usually collected at regular intervals.This research aims to identify this important topic for those who don't have any background about it or want to know more and clearly understand it.

The types of time series analysis. Time series data can be classified into two main categories: stock time series data and flow time series data, the types of time series analysis types, which include curve fitting, descriptive analysis, segmentation, forecasting, exploratory analysis, explanatory analysis, functional analysis and trend analysis. It’s very important for answering business questions.

After discussing the types of time series we present their models and techniques, which are fundamentally important in various practical domains. It shows a variety of methods to study data like decompositional models, smoothing-based models, moving-average model, exponential smoothing model, Holt-Winters method and autoregressive integrated moving average (ARIMA) model. The section includes an elaborate definition of the ARIMA model and its standard notation, and talks about Box-Jenkins ARIMA models.

Finally, the thing that should be mentioned is the applications of time series. People can make use of time series in many domains. For instance: in weather forecasting, people use time series to predict what the weather would be in the next hour, day or week. In the financial and business domain, time series and forecasting are essential processes for explaining the dynamic and influential behavior of financial markets. Other domains in which time series analysis is useful are astronomy and medicine.


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