A Time Series is typically defined as a series of values that one or more variables take over successive time periods. For example, sales volume over a period of successive years, average temperature in a city over months etc. If the series is about only one variable, it is called Univariate Time Series. If the series lists values of more than one variables over different points of time, it is called Multivariate Time Series. In the example we deal in this blog, we will deal with a univariate time series.
. checking for duplicates and null values
. doing cleaning process (data wrangling) and data modeling
. data transformation (changing data type)
.checking for outliers and skewness of the series.
. using the plot to the series data next understand the series has happened(behavior) over time.
.checking each component of the series to know the behavior of the series(Trend/Seasonality/cyclic).
.Transforming series data into stationary from non-stationary next, do the dickey fuller test for stationary.
.making keeping differentiate plots and ACF + PACF plots side by side for finding autoregression and moving averages points which are correlated with past values.
.separetion of series into 2 parts.
forecasting for future values
conclusion.