Before diving into how forecasting is to be done, let's cover the definition of Time, Cities and Time
series forecasting First Time Cities is simply a series of data points where time order.
This means that we pick one or more variables and know their values over time.
Here are some examples of time to these data.
The first table shows profit over last five years,
so you can see profit from 2001 to 2005.
And for each year we have the profit value in the second table.
We have data noted for temperature for every five minutes.
This is and other times it is.
But the third example is not four times it is.
It is cross-sectional data.
Cross-sectional data is a set of measurements taken at one point in time or time is not considered as
a factor.
For example, in this third day, will we have latitude and temperature values of different cities?
These maybe have been noted for one particular time period to time cities is simply a series of data
points.
With Time 90s forecasting is use of model to predict future values based on previously observed values.
So we will look at actual historical values in the cities and try to build a model that can predict
values based on these historical values.
For example, this is previous year's sales performance.
Future sales forecast can be generated for the coming time period.
We are going to see different types of models that can help us make these predictions.