Time series_ is a sequence of time-based data points collected at specific intervals of a given phenomenon that undergoes changes over time. In other words, time series is a sequence taken at consecutive equally spaced points in the time period._

As a example, we can present few time series data sets in different domains such as pollution levels, Birth rates, heart rate monitoring, global temperatures and Consumer Price Index etc. At the processing level, above datasets are tracked, monitored, down sampled, and aggregated over time.

There are different kind of time series analysis techniques in the big data analytical field. Among them few are,

• Autoregression (AR)
• Moving Average (MA)
• Autoregressive Moving Average (ARMA)
• Autoregressive Integrated Moving Average (ARIMA)
• Seasonal Autoregressive Integrated Moving-Average (SARIMA)

ARIMA Model

ARIMA Model is simple and flexible enough to capture relationship we would see in the data and It aims to explain the autocorrelation between the data points using past data. We can decompose the ARIMA model as follow to grab the key elements of it.

• **AR: _Auto regression. _**This is a model that uses the dependent relationship between the data and the lagged data.
• **I:_ Integrated. _**The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.
• **MA: _Moving average. _**A model that uses the relationship between the observations and the residual error from the moving average model applied to lagged observations.

Dataset Explanation

Exploratory Analysis

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