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    Time series is a sequence of numerical data points in successive order. It can be taken on any data that change over time. Time series analysis can be used to determine how changes associated with a given data point compared to other variables over the same period of time. It is divided into two classes. These are; Frequency domain method- it includes Wavelet analysis and spectral analysis. Time domain method-This method includes both the cross-correlation and auto correlation method.

    Time series forecasting uses information based on past data to predict future activities.

    Time series analysis manipulates data and extracts meaningful characteristics and statistics. The three main basic models under time series are;

    • AR
    • MA
    • ARMA

    The following characteristics are considered when looking at a time series;

    • Trend- Does the measurements tend to increase or decrease over time on average?
    • Seasonality-Is there a regular repetition of patterns related to calendar time such as days, weeks, months, quarters or seasons?
    • Outliers- Unlike in regression where outliers are far away from your line, in time series data your outliers are far away from your other data
    • Long-run cycle- These are periods that are not associated with seasonality factors
    • Constant variance- Is the variance constant or variable over time?
    • Abrupt changes- Are there any abrupt changes to either the level of the series or the variance

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    • Uses of time series
    • Autocorrelation
    • Spectral analysis
    • Estimation
    • Financial time series and the arch model
    • Stationary and non-stationary
    • Bivariate time series
    • Stochastic modelling and Bayesian inference
    • Diagnostic checking and linear prediction
    • Time series analysis and forecasting
    • Parameter estimation
    • Model identification
    • Vector autoregressive moving average with exogenous inputs (VARMAX)

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    Time series is a tool that measures data points at successive points and is maintained at significant time levels. It involves lots of calculations and data analysis. It has 3 main objectives;

    • Forecasting
    • Modelling
    • characterization

    Time series is applied in subjects such as statistics, processing of signals, recognition of patterns, econometrical studies, finance and mathematical interpretations, weather forecasting, prediction of earthquakes, engineering and communications. Time series is a very interesting subject and requires extensive study.

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