Literature review plans

Finally I was able (almost) to install the TexLive on my laptop. The installer never finished so I don't really know if everything works (the 00-00 example compiles though). I'm running Windows Vista and it looks like the TexLive installer has permission issues with non-Administrative account + there seems to be a bug with installer when it tries to use system Perl instead of one shipped with installer (which causes some libraries and runtime issues). It took me some time to figure out both issues and I don't really see advantages of TexLive versus MikTEX at this point. The one more disadvantage I see is the lack of the DVI viewer in the TexLive distro, looks like I need to install the viewer, but I'm not sure if I will need it, so we'll see further if I will need one.

From other things I've set up all the Java stuff, updated libraries and Hackystat, checked out all the latest sources etc. and backed up the whole thing, just in case.
So, system is ready to go.

Most of the time I spent on putting together the outline for the literature review. I am seeing the purpose of this writing to be a comprehensive walkthrough through the field of the time-series analysis outlining the milestones and major discoveries and connecting them with my research. I found that I've totally missed some major things in the time-series analysis (funny huh?) and filling these gaps with reading and collecting the literature.

Following is the draft plan, I'm working on the third part and since it is based on the material from the part 2, I am changing its flow too.

Literature review plan

1. 
   

   Introduction. (definitions, research field boundaries and common applications)

   
   
   
   
   1. Introduction to time series.
      
      
      
      1. Data sources, time-series representation and common applications
         
         (the time series “origin”, common representation and mainstream applications)
      
      
      2. Streaming time-series.
         
         Time series as streams.
      
      
      3. Time-series databases and indexing
         
         (examples of existing time-series collections (+ the Hackystat sensorbase) and common time-series databases toolkit for time series data storage, search and retrieval)
      
      
      
      
   
   
   2. Classical time series analyses.
      
      
      
      1. General exploration & description
         
         (time series descriptive exploration and common tools used: spectral analysis, autocorrelation, trends, periodicity (+ Hackystat Telemetry, + Hackystat Zorro?, + Hackystat Trajectory))
      
      
      2. Prediction and forecasting
         
         (stochastic modeling: AR, MA, ARMA, ARIMA and uses (+ Hackystat Trajectory))
         
      
      
   
   
   3. Time series similarity (homogeneity) based analyses.
      
      
      
      1. The speech and handwriting recognition.
         
         (pioneering the area of DTW, LCS and HMM)
      
      
      2. Sign language, motion and gesture recognition.
         
         (ongoing research)
      
      
      3. Trajectory patterns recognition, surveillance applications, shape recognition.
         
         (modern applications)
      
      
      
      
   
   
   
   


2. 
   

   Time series similarity-based analyses and algorithms
   
   (known research tools, implemented applications and up-to date research directions)

   
   
   
   
   1. Similarity metrics
      
      
      
      1. Euclidean distance.
         
         (application and problem of normalization)
      
      
      2. Hamming and Edit distances.
         
         (the formal introduction of edit distance, time-series transformations)
      
      
      
      
   
   
   2. Similarity-finding algorithms
      
      
      
      1. DTW
      
      
      2. LCS
      
      
      
      
   
   
   3. Methods (whole and sub sequence applications)
      
      
      
      1. Clustering
      
      
      2. Indexing
      
      
      3. Classification
      
      
      4. Anomaly detection
      
      
      
      
   
   
   4. Known state of the art applications.
   
   
   
   


3. 
   

   Possible application of the algorithms and methods to the Hacvkystat Telemetry Streams
   

   
   
   
   
   1. Similarity search in the Sensorbase
      
      (the search for similarity using the raw telemetry data stored within the sensorbase)
   
   
   2. Telemetry Streams data Indexing
      
      (defining the Telemetry patterns, indexing raw telemetry data using definitions and conducting search be means of indices and Edit distance)
   
   
   3. Live Telemetry Stream analysis and features
      
      (patterns, anomaly detection)