Functional Data Analysis Tools
  for the Livestock Scientist
Overview
What is functional data analysis (fda)?


How can fda be useful to you?


Application example.
The Nature of GPS Tracking
            Data

Ordered
Spatio-Temporal Correlation
Information-Dense
Quasi-Periodic
Incomplete
Errors
Constrained Support
Principles of FDA Pt. I.
Each datum is a functional,



                                                        []
     v (t )I ={v (t 1 ) , v (t 2 ) ,… , v (t n )}.      v (t )1
The key is to transform the data,                    D= v (t )2
              v (t)I → 〈Φ(t) , v (t )〉 Φ(t )              ⋮
                                                        v (t )n
Principles of FDA Pt. II.
                  Fourier Transform
                   1                    kt
            F (ω)=( ) ∑ v (t )exp[−i2 π( )]
                   N                    N
Suited for periodic trends such as diurnal activity patterns.



Correlates sinusoidal functions sin (ω t ) with the signal v (t )



Creates a set of coefficients α whose magnitude
indicate the “strength” of the frequency ω
Principles of FDA Pt. III.
                Wavelet Transform

        v (t)=∑ c j , k ϕ j , k (t )+∑ ∑ d j , k ψ j , k (t)
                         0   0




  Suited to non-periodic data with lots of
“jumps”.
 Enables a multi-resolution analysis.
 Coefficients c j , k encode the “smooth” and d j , k
                     0


the “detail”.
Potential Uses of FDA for SELM
  Trajectory Clustering
  Outlier Detection
  State Classification
  Interpolation
  Smoothing
  Multi-resolution Analysis
Application: Parasite Burden
 Can Livestock Tracking Collars tell us if sheep
have parasite burden?
Application: Parasite Burden
 Support-Vector Interpolation and Smoothing
                                    ̂
          v (t )={x (t ) , y (t )}→ v (t )
Application: Parasite Burden
Adaptive Basis Using Singular Value Decomposition
                         T
               D=U Λ V
The Future
FDA Livestock Tracking Software


Semi-automated analysis within Software


Aberrant Behaviour Warnings


Social Networks – very high-dimensional fda
Thank you!

Selm Falzon Compressed

  • 1.
    Functional Data AnalysisTools for the Livestock Scientist
  • 2.
    Overview What is functionaldata analysis (fda)? How can fda be useful to you? Application example.
  • 3.
    The Nature ofGPS Tracking Data Ordered Spatio-Temporal Correlation Information-Dense Quasi-Periodic Incomplete Errors Constrained Support
  • 4.
    Principles of FDAPt. I. Each datum is a functional, [] v (t )I ={v (t 1 ) , v (t 2 ) ,… , v (t n )}. v (t )1 The key is to transform the data, D= v (t )2 v (t)I → 〈Φ(t) , v (t )〉 Φ(t ) ⋮ v (t )n
  • 5.
    Principles of FDAPt. II. Fourier Transform 1 kt F (ω)=( ) ∑ v (t )exp[−i2 π( )] N N Suited for periodic trends such as diurnal activity patterns. Correlates sinusoidal functions sin (ω t ) with the signal v (t ) Creates a set of coefficients α whose magnitude indicate the “strength” of the frequency ω
  • 6.
    Principles of FDAPt. III. Wavelet Transform v (t)=∑ c j , k ϕ j , k (t )+∑ ∑ d j , k ψ j , k (t) 0 0 Suited to non-periodic data with lots of “jumps”. Enables a multi-resolution analysis. Coefficients c j , k encode the “smooth” and d j , k 0 the “detail”.
  • 7.
    Potential Uses ofFDA for SELM Trajectory Clustering Outlier Detection State Classification Interpolation Smoothing Multi-resolution Analysis
  • 8.
    Application: Parasite Burden Can Livestock Tracking Collars tell us if sheep have parasite burden?
  • 9.
    Application: Parasite Burden Support-Vector Interpolation and Smoothing ̂ v (t )={x (t ) , y (t )}→ v (t )
  • 10.
    Application: Parasite Burden AdaptiveBasis Using Singular Value Decomposition T D=U Λ V
  • 11.
    The Future FDA LivestockTracking Software Semi-automated analysis within Software Aberrant Behaviour Warnings Social Networks – very high-dimensional fda
  • 12.