Dependency
Temporal dependency: future observations are impacted by previous observations
Spatial dependency: observed points being further away from each other or closer together than expected from random data
Time Series Concepts
ARIMA: autoregressive integrated moving average, model used for predicting future observations based on past observations, assumes future model follows from past so unexpected changes would not be predicted
p: # of autoregressive terms
d: # of nonseasonal differences needed for stationarity
q: # of lagged forecast errors in prediction equation
Stationary: observation properties do not depend of time observed
First difference: changes from period at time t-1 to time t
First order autoregression: using one previous value to predict future values in time-series data
Charts for Plotting Time
A line chart can be used to show the continuous change in a y-axis variable over time plotted on the x-axis, with line color or line type denoting relevant categorical variables
An animation can be used for any plot type to show change in time with each frame corresponding to the data at one time
A line plot can be used with data mapped on an x-y plane where each subsequent data point is observed at a different time and neighbor data points are connected by a line to show continuity