Time Series Analysis With Documentation And Steps I Follow For Analytics Projects.

This article is originally published at https://laranikalranalytics.blogspot.com/

To do this I will create a prediction of the open values for Bitcoin in the next 3 days.

The process I follow is based on CRISP-DM methodology: https://www.datasciencecentral.com/profiles/blogs/crisp-dm-a-standard-methodology-to-ensure-a-good-outcome

1.- Planning the activities.

To plan the activities I use a spread sheet document, below I show the spread sheet sample, if you would like the document, please go to the next link:
https://github.com/LaranIkal/R-ANALYTICS/blob/master/BitCoinPredictionsAdmin%26FollowUp.ods

Activity	Activity Description	DueDate	Activity Owner	Status	Comments
Functional Requirement Specification	A Text Document explaining the objectives of this project.	4/19/2018	Carlos Kassab	Done
Get Data For Analysis	Get initial data to create feasibility analysis	4/19/2018	Carlos Kassab	Done
ETL Development	ETL to get final data for next analysis	4/20/2018	Carlos Kassab	Done	2018/04/19: In this case, there is not ETL needed, the dataset was downloaded from kaggle: https://www.kaggle.com/vivekchamp/bitcoin/data
Exploratory Data Analysis	Dataset summary and histogram to know the data normalization	4/20/2018	Carlos Kassab	In progress
Variables frequency	Frequency of variable occurrence( frequency of values change, etc. )	4/20/2018	Carlos Kassab	Done	2018/04/19: We have already seen that our values change every day.
Outliers Analysis	Analysis of variability in numeric variables, show it in charts and grids..	4/20/2018	Carlos Kassab	In progress
Time Series Decomposition	– Getting metric charts, raw data, seasonality, trend and remainder.	4/20/2018	Carlos Kassab	In progress
Modelling	Create the analytics model	4/25/2018	Carlos Kassab	Not Started
SQL View Development	For Training, Validation And Testing	NA	Carlos Kassab	Not Started	2018/04/19: No SQL view needed, everything is done inside the R script.
Model Selection	By using random parameters search algorithm, to find the right model to be used for this data.	4/25/2018	Carlos Kassab	Not Started
Model fine tunning.	After finding the right algorithm, find the right model parameters to be used.	4/25/2018	Carlos Kassab	Not Started
Chart Development	Final data chart development	4/25/2018	Carlos Kassab	Not Started
Data Validation	Run the analytics model at least 2 weeks daily in order to see its behavior.	NA	Carlos Kassab	Not Started
Deployment	Schedule the automatic execution of the R code.	NA	Carlos Kassab	Not Started

The first activity is the functional specification, this would be similar to business understanding in the crisp-dm methodology.

I use a text document, for this analysis, you can get the document from this link:
 https://github.com/LaranIkal/R-ANALYTICS/blob/master/BitCoinPredictionRequirementSpecification.odt

Now, the next step is to get the data for analysis and create the ETL script, in this case we just got the data from kaggle.com as mentioned in the documentation but, no ETL script was needed.

So, we have our data, now we are going to analyze it, we will do all the activities mentioned in yellow in the grid above. I did this in a MarkDown document, here you can see the HTML output:

https://github.com/LaranIkal/R-ANALYTICS/blob/master/Bitcoin%20Data%20Analysis.pdf

It seems that github is not serving big html files but, anyway I am including the html link:

https://github.com/LaranIkal/R-ANALYTICS/blob/master/BitcoinDataAnalysis.html

Note. At the end, in order to have everything together I included the time series algorithms in the same rmd file that creates BitcoinDataAnalysis.html file.

You can get all the sources from, this is the best way if you are interested in seeing the analysis:

https://github.com/LaranIkal/R-ANALYTICS

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This article is originally published at https://laranikalranalytics.blogspot.com/
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