Class: Statsample::TimeSeries::Arima::KalmanFilter

Inherits:
Object
  • Object
show all
Includes:
GSL::MultiMin, Statsample::TimeSeries
Defined in:
lib/bio-statsample-timeseries/arima/kalman.rb

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods included from Statsample::TimeSeries

arima

Constructor Details

#initialize(ts = [].to_ts, p = 0, i = 0, q = 0) ⇒ KalmanFilter

Returns a new instance of KalmanFilter.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 11

def initialize(ts=[].to_ts, p=0, i=0, q=0)
  @ts = ts
  @p = p
  @i = i
  @q = q
  ks #call the filter
end

Instance Attribute Details

#ar ⇒ Object (readonly)

Returns the value of attribute ar.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 10

def ar
  @ar
end

#i ⇒ Object

Returns the value of attribute i.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 9

def i
  @i
end

#ma ⇒ Object (readonly)

Returns the value of attribute ma.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 10

def ma
  @ma
end

#p ⇒ Object

Returns the value of attribute p.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 9

def p
  @p
end

#q ⇒ Object

Returns the value of attribute q.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 9

def q
  @q
end

#ts ⇒ Object

Returns the value of attribute ts.



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 9

def ts
  @ts
end

Class Method Details

.log_likelihood(params, timeseries, p, q) ⇒ Object

log_likelihood

Computes Log likelihood on given parameters, ARMA order and timeseries params: -params::array of floats, contains phi/theta parameters -timeseries::timeseries object -p::integer, AR(p) order -q::integer, MA(q) order Returns: LogLikelihood object Usage: s = (1..100).map { rand }.to_ts p, q = 1, 0 ll = KalmanFilter.log_likelihood(, s, p, q) ll.log_likelihood #=> -22.66 ll.sigma #=> 0.232



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 100

def self.log_likelihood(params, timeseries, p, q)
  Arima::KF::LogLikelihood.new(params, timeseries, p, q)
end

Instance Method Details

#ks ⇒ Object

Kalman Filter

Function which minimizes KalmanFilter.ll iteratively for initial parameters Parameters: -timeseries: timeseries object, against which the ARMA params has to be estimated -p: order of AR -q: order of MA Usage:

  • ts = (1..100).to_a.to_ts

  • KalmanFilter.ks(ts, 3, 1)

NOTE: Suceptible to syntactical change later. Can be called directly on timeseries. NOTE: Return parameters



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 35

def ks
  initial = Array.new((@p+@q), 0.0)

  my_f = Proc.new{ |x, params|
    #In rb-gsl, params remain idle, x is varied upon
    #In R code, initial parameters varied in each iteration
    #my_func.set_params([(1..100).to_a.to_ts, p_value, q_value])
    timeseries = params[0]
    p,q = params[1], params[2]
    params = x
    #puts x
    -Arima::KF::LogLikelihood.new(x.to_a, timeseries, p, q).ll
    #KalmanFilter.ll(x.to_a, timeseries, p, q)
  }
  np = @p + @q
  my_func = Function.alloc(my_f, np)
  my_func.set_params([@ts, @p, @q])
  x = GSL::Vector.alloc(initial)
  ss = GSL::Vector.alloc(np)
  ss.set_all(0.1)

  minimizer = FMinimizer.alloc("nmsimplex", np)
  minimizer.set(my_func, x, ss)
  status = GSL::CONTINUE
  iter = 0
  while status == GSL::CONTINUE && iter < 100
    iter += 1
    begin
      status = minimizer.iterate()
      status = minimizer.test_size(1e-2)
      x = minimizer.x
    rescue
      break
    end
  #  printf("%5d ", iter)
  #  for i in 0...np do
  #    puts "#{x[i]}.to_f"
  #    #printf("%10.3e ", x[i].to_f)
  #  end
  #  printf("f() = %7.3f size = %.3f\n", minimizer.fval, minimizer.size)
  end
  #
  @ar = (p > 0) ? x.to_a[0...p] : []
  @ma = (q > 0) ? x.to_a[p...(p+q)] : []
  x.to_a
end

#to_s ⇒ Object 



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# File 'lib/bio-statsample-timeseries/arima/kalman.rb', line 19

def to_s
  sprintf("ARIMA model(p = %d, i = %d, q = %d) on series(%d elements) - [%s]",
          @p, @i, @q, @ts.size, @ts.to_a.join(','))
end