here is the part 1 intro:
Regression Models, both linear and logistic are an inevitable part of Analytics industry. Take a flashback & recall, when did you built your last Time Series model. Time series models are very useful models when you have serially correlated data. In case you have never built a time series model or you struggle with some concepts of time series models, you have landed at the right page.and this the part 2 intro:
This is the second part of the step by step guide to Time Series Modelling. In the first part, we looked at basics of time series, stationary series, random walk and Dicky Fuller test. If you have not read this article, I would suggest to go through that first.My suggestion read and test it Part 1 & Part 2
In this article we will talk about handling time series data on R. Our scope of this article will be restricted to data exploring in a time series type of dataset and not go to building time series models. In this article I have used an inbuilt dataset of R called AirPassengers. The dataset consists of monthly totals of international airline passengers, 1949 to 1960. This article will help you explore the data step by step and we will make predictions based on this data for the number of passengers post 1960 in next few articles.