Project Meeting (27/10/2017)

Topics Discussed

Data

Variables

Meaning of variables in the data set
  • CPI: Consumer price index.
  • GDP: Gross domestic product
  • UR: unemploymeent rate.
  • IR Policy Rate - Interest rate (Possibly inflation subtracted from  nominal interest rate)
  • LR10: Possibly 10 year loan rate.
  • LR10 - IR: IR subtracted from LR10.
  • Exrate Euro for 1 USD - exchange rate b/w euro and dollar.
Wheather or not data has been preprocessed in any way - about to find out.

Data analysis and preprocessing

Should check if data is stationary or not. Identify any trend and seasonality. Test significance of each variable using a benchmark architecture (simple MLP). Explore different preprocessing techniques and if they improve performance on benchmark architecture. Longer term question - do we need more data? 

Report

Possible structure of Problem Analysis?

  • Data - what kind and where froerform m?
  • Stationarity, Seasonality, Trend
  • Neural network architecture

Tasks Completed

Tasks for Next Week

Priority

  • Learn about stationarity, trends and seasonality.
  • Analyse data - perform simple regression, figure out if it's stationary or not, identify trends and seasonality.
  • Create benchmark MLP architecture and perform experiments to determine significance of variables and get a benchmark accuracy.
  • Write up analysis and initial experiments.

Leftover

  • Read deep learning book on RNNs.
  • Read Using R for Time Series Analysis 
  • Read more papers.
  • Write up literature review outline in Latex and further break it down into smaller topics.
  • Figure out correct formatting for project report (fonts, margins etc.) and modify document accordingly.

Additional Notes

Probably a good idea to create separate blog posts for work completed and post it before the meeting so it could be discussed during the meeting. In next meeting going to discuss scheduling project and assessments.

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