Guide to Time Series Model

Time Series is a set of data points over a period used to analyze and forecast the future. Time is the independent variable.

Characteristics of Time Series Model

Autocorrelation: Is the similarity between observation as a function of the time lag between them.

Seasonality: Periodic fluctuations for example online sales peak during Christmas before slowing down.

Stationary: Here is when the statistical properties remain constant over time. It can be tested using the Dickey-Fuller test by evaluating the null hypothesis to determine if a unit root is present.

Time Series Analysis Types

Classification: It identifies and assigns categories to the data.

Curve Fitting: It plots data on a curve to investigate the relationships between variables in the data.

Descriptive Analysis: Identifies patterns such as trends and seasonal variations.

Explanative analysis: It attempts to comprehend the data and the relationships within it and cause and effect.

Segmentation: It splits the data into segments to reveal the source data's underlying properties.

Time series analysis can be used in -
Rainfall measurements
Heart rate monitoring (EKG)
Brain monitoring (EEG)
Quarterly sales
Stock prices
Automated stock trading
Industry forecasts
Interest rates