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Time Series

In the basic explanations, time series refers to a set of discreet data observed over a period of time. The observed data is recorded over specific time intervals. The time in this case is considered as an independent variable, and the major goal of the time series studies is to predict the future. Other important goals involve interpolation, smoothing, and modelling of the structures. Time series is mainly defined by the ordering of the observations as the changes in the order leads to changing of the data meaning.

Types of Time Series Data

There are three major characteristics of time series: trend, seasonality and autocorrelation. Trend involves the changes that are evolving slowly in the series level. The changes in this category can be modelled by either low-frequency sinusoids or low-order polynomials. Usually, trends are long-term movements within the series. Seasonality on the other hand refers to regular repeating patterns of eithers lows or highs that are that are related to time events such as months, days, and seasons among others.  Autocorrelation involves a function of the time lag between the similarity showed between the observations. It is important in distinguishing the time series other statistical analysis branches. Stationarity (time series statistical properties remaining unchanged over time) is another crucial characteristic.

Manipulation of Time Series Data

Time series analysis involves methods and techniques used to analyze the time series data with aims of extracting meaningful characteristics from the data. One of the models used to predict that values of the future is the time series forecasting. The methods used in time series analysis is divided into two: time-domain and frequency-domain methods. Time-domain methods include cross-correlation and auto-correlation analysis while the frequency-domain methods include wavelet and spectral analysis. Time series is modeled using various methods to make predictions. There are various methods used in modelling and the three broad classes are: the moving average, Integrated, and autoregressive models. S Other models include: exponential smoothing, as well as the autoregressive integrated moving average model (ARIMA) and autoregressive moving average which combines the three broad models.

Time series is a vast field that is applied in lots of real-life cases of scientific investigations. Since time is a physical concept virtually present in all activities, the analysis done is important in studying trends, evolving behavior, changes and rhythms among other applications. Prediction of consumer expectations and voting patterns, forecasting of earthquakes and rainfall are some of the basic applications of time series analysis based on known past events.