# Thames Flux

It is roughly 150 miles from the source of the Thames to Kingston Bridge. If we assume that it flows at about 2 miles per hour then the water at Thames Head will have reached Kingston very roughly at $\frac{150}{24\times 2} \approxeq 3$ days.

The Environmental Agency measure the flux at Kingston Bridge on a twice daily basis. Can we predict this? In the first instance without any other data and using our observation that Thames flushes itself every 3 days, let us try

$\displaystyle X_t = \theta_1 X_{t-1} + \theta_2 X_{t-2} + \theta_3 X_{t-3} + \epsilon_t$

where $X_t$ is the flux on day $t$ and $\{\epsilon_t\}_{t \in \mathbb{N}}$ are independent normal errors with mean $0$ and variance some given value $\sigma^2$.

# Kalman

As it stands, our model is not Markov so we cannot directly apply techniques such as Kalman filtering or particle filtering to estimate the parameters. However we can re-write the model as

$\displaystyle \begin{bmatrix} \theta_1^{(t)} \\ \theta_2^{(t)} \\ \theta_3^{(t)} \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} \theta_1^{(t-1)} \\ \theta_2^{(t-1)} \\ \theta_3^{(t-1)} \end{bmatrix} + \begin{bmatrix} \eta_{t} \\ \eta_{t} \\ \eta_{t} \end{bmatrix}$

$\displaystyle y_t = \begin{bmatrix} x_{t-1} & x_{t-2} & x_{t-3} \end{bmatrix} \begin{bmatrix} \theta_1^{(t)} \\ \theta_2^{(t)} \\ \theta_3^{(t)} \end{bmatrix} + \epsilon_{t}$

Note that the observation map now varies over time so we have modify our Kalman filter implementation to accept a different matrix at each step.

> {-# OPTIONS_GHC -Wall                     #-}
> {-# OPTIONS_GHC -fno-warn-type-defaults   #-}
> {-# OPTIONS_GHC -fno-warn-unused-do-bind  #-}
> {-# OPTIONS_GHC -fno-warn-missing-methods #-}
> {-# OPTIONS_GHC -fno-warn-orphans         #-}

> {-# LANGUAGE DataKinds                    #-}
> {-# LANGUAGE ScopedTypeVariables          #-}
> {-# LANGUAGE RankNTypes                   #-}
> {-# LANGUAGE TypeOperators                #-}
> {-# LANGUAGE TypeFamilies                 #-}

> module Autoregression (
>     predictions
>   ) where

> import GHC.TypeLits
> import Numeric.LinearAlgebra.Static
> import Data.Maybe ( fromJust )

> import qualified Data.Vector as V

> inv :: (KnownNat n, (1 <=? n) ~ 'True) => Sq n -> Sq n
> inv m = fromJust $linSolve m eye  > outer :: forall m n . (KnownNat m, KnownNat n, > (1 <=? n) ~ 'True, (1 <=? m) ~ 'True) => > R n -> Sq n -> [L m n] -> Sq m -> Sq n -> Sq n -> [R m] -> [(R n, Sq n)] > outer muPrior sigmaPrior bigHs bigSigmaY bigA bigSigmaX ys = result > where > result = scanl update (muPrior, sigmaPrior) (zip ys bigHs) > > update :: (R n, Sq n) -> (R m, L m n) -> (R n, Sq n) > update (xHatFlat, bigSigmaHatFlat) (y, bigH) = > (xHatFlatNew, bigSigmaHatFlatNew) > where > v :: R m > v = y - bigH #> xHatFlat > bigS :: Sq m > bigS = bigH bigSigmaHatFlat (tr bigH) + bigSigmaY > bigK :: L n m > bigK = bigSigmaHatFlat (tr bigH) (inv bigS) > xHat :: R n > xHat = xHatFlat + bigK #> v > bigSigmaHat :: Sq n > bigSigmaHat = bigSigmaHatFlat - bigK bigS (tr bigK) > xHatFlatNew :: R n > xHatFlatNew = bigA #> xHat > bigSigmaHatFlatNew :: Sq n > bigSigmaHatFlatNew = bigA bigSigmaHat (tr bigA) + bigSigmaX  We can now set up the parameters to run the filter. > stateVariance :: Double > stateVariance = 1e-8  > bigSigmaX :: Sq 3 > bigSigmaX = fromList [ stateVariance, 0.0, 0.0 > , 0.0, stateVariance, 0.0 > , 0.0, 0.0, stateVariance > ]  > bigA :: Sq 3 > bigA = eye  > muPrior :: R 3 > muPrior = fromList [0.0, 0.0, 0.0]  > sigmaPrior :: Sq 3 > sigmaPrior = fromList [ 1e1, 0.0, 0.0 > , 0.0, 1e1, 0.0 > , 0.0, 0.0, 1e1 > ]  > bigHsBuilder :: V.Vector Double -> [L 1 3] > bigHsBuilder flows = > V.toList$
>   V.zipWith3 (\x0 x1 x2 -> fromList [x0, x1, x2])
>   (V.tail flows) (V.tail $V.tail flows) (V.tail$ V.tail $V.tail flows)  > obsVariance :: Double > obsVariance = 1.0e-2  > bigSigmaY :: Sq 1 > bigSigmaY = fromList [ obsVariance ]  > predict :: R 3 -> Double -> Double -> Double -> Double > predict theta f1 f2 f3 = h1 * f1 + h2 * f2 + h3 * f3 > where > (h1, t1) = headTail theta > (h2, t2) = headTail t1 > (h3, _) = headTail t2  > thetas :: V.Vector Double -> [(R 3, Sq 3)] > thetas flows = outer muPrior sigmaPrior (bigHsBuilder flows) > bigSigmaY bigA bigSigmaX (map (fromList . return) (V.toList flows))  > predictions :: V.Vector Double -> V.Vector Double > predictions flows = > V.zipWith4 predict > (V.fromList$ map fst (thetas flows))
>   flows (V.tail flows) (V.tail \$ V.tail flows)


# How Good is Our Model?

If we assume that parameters are essentially fixed by taking the state variance to be e.g. $10^{-8}$ then the fit is not good.

However, if we assume the parameters to undergo Brownian motion by taking the state variance to be e.g. $10^{-2}$ then we get a much better fit. Of course, Brownian motion is probably not a good way of modelling the parameters; we hardly expect that these could wander off to infinity.