Group 10 Report
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Contents
Motivation and Objective
Problem
Many analysis methods and algorithms out there fail to be utilized or optimized by the users. They are either poorly derived with great visualization or accurately derived with poor visualization. R has over 10000 packages that support visualization for advanced analysis too. There is a gap in the potentiality of R and what we use in day to day life. The mainstream packages are very few which cover basic analysis and algorithms. There are many data that require advanced analysis to come up with more accurate and dependable results.
Especially, many economic data and macro economic data have a lot of scope statistically to be analysed and give major indications on the economy and other influenced markets. Even if they could be built they do not have easy access and usability to such application. So the people are loss are economists and financiars who lacks the access to applications that can easily and quickly analyse the required data in the advanced model through efficient visualuatization tools.
Solution
We have put together the best method of analysing time series in a most efficient way.The advanced time series analysis is based on state of the art time series clustering and also forecasting based on ARIMA model. This provides an accurate model for analysis of time series clustering based on DTW algorithm. This method has been visualised in different way to help in easier comparison and understanding of the characteritics of the data. The Rshiny application makes it easier for the user to choose the different the different option tha is part of the analysis. Just by few click the result is varied based on the chosen algorithm.
Housing Price Index is a major economic factor that not just depicts the housing market but also the economy.The housing prices analysis of 48 cities of China. To understand their trend and compare them with the other cities based on the distance measure. The advanced time series analysis methods helps in understanding the response of the cities and doing a comparative study over a period.
Data Preparation and Packages Used
Data Design
The data is taken from CEIC page and was cleaned and the t
R Packages Used
Analysis Method
Clustering
Number of CLusters
Type of Clustering
Distance Measure
Forecasting
Visualization
Application
Future Scope
The future scope is that we can recommend the number of selected cluster based on CVI and best methodology based on compactness and separation within clusters. Currently it is used for just HPI but other data can be added to expand the property market analysis. The data can also be any other time series data. This algorithm is even used in machine learning that any data being time bound can be feeded into the system to allow for analysis. The scope can be expanded, in the future, the application can use for larger region such as province, country even for intercontinental.
