13 Jan
13Jan

As I announced last week that, I am learning Time Series Analysis and share my knowledge on it, could not find a better way of writing a blog too, to describe what steps I did take to learn it. So, this is the phase 1 of my learning -

So first thing I did, obviously "Google" to see whats happening latest in this particular type of data modeling. This is the fact I always love about being in open source. What you need is just a computer and internet :-).

I searched for "Time Series Analysis" and wanted to see what google search engine recommends me, as top 10 items in the list on page 1 of my search. So I found below items-

In detail one tutorial, on How To Identify Patterns in Time Series Data: Time Series Analysis http://www.statsoft.com/textbook/time-series-analysis

Found one handbook for time series analysis -https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm

Blog - https://towardsdatascience.com/the-complete-guide-to-time-series-analysis-and-forecasting-70d476bfe775

Then found one pdf explaining statistical analysis for Time Series -http://www.stat.columbia.edu/~rdavis/lectures/Session6.pdf

as well as - https://www.statisticssolutions.com/time-series-analysis/

Tutorial along with R-code on coding club -https://ourcodingclub.github.io/2017/04/26/time.html#challenge

A Complete Tutorial on Time Series Modeling in R by Analytics Vidhya https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/

Lots of links for Scholarly Articles for Time Series Analysis and then one by default thing  Time series - Wikipedia

If I check further it will show lots of similar things of different blogs, different articles, different titles, different coding, different tools and so on. But I don't need too much to start my learning.

What one needs to start learning it first? If you are completely new and don't know any programming I would say read blogs or articles or papers. Then if you find it interesting watch some videos and then take some full course on programming or time series analysis [Reach out to me if you need any guidance in that].

As I am interested to see python coding specifically, none of the item listed in my first search are useful to me, except getting some basic understanding on Time Series Analysis by reading. Surprisingly, I didn't find any Time Series Analysis using Python Code on first page of the search list. So I modified my search as "Time Series Analysis using Python" and found everything related to Python and Time Series Analysis/Forecasting. Okies, first step of my learning phase find what I am interested and find free content/tutorials/blogs/articles/codes is complete. 

Now, second step is scan through these links see what is there? You might find something interesting. Out of top 10 links from google I shortlisted 5 as listed below -

https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b

https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/

https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/

https://www.kaggle.com/kashnitsky/topic-9-part-1-time-series-analysis-in-python

https://ourcodingclub.github.io/2019/01/07/pandas-time-series.html

But still, I am missing something explaining the statistical part of it - so might take one of the items listed from my first search. May be to start with this one -https://www.statisticssolutions.com/time-series-analysis/

Also watch some of the YouTube Videos and at the end we will recap at least how much I am able to learn with some fresh dataset.

Okies, I am all set to at least start my learning for Time Series Analysis. Phase 1 Completed.

I would like to hear how did you approach for your learning or what are you referring to learn, so do write a post or a blog or share your learning and tag with #technicaltopictuesday, so that everyone can learn as well as you might come across better approach of learning.

Until we meet, Happy Leading and Let's Lead Together! See you guys soon!