I have 3 numeric variables $x$, $y$, and $z$. Specifically $x$ , $y$ are tachometer readings. I am given $x$ and $y$ and I need to predict $z$ (the velocity of a vehicle). I have built a model that uses standard methods (random forest) and this achieves a decent ($80\%$) accuracy. It achieves this despite the dependence of my $z$ values.

However I have since discovered that there is a function $f$ $st$ $f(z_{t-1}) = z_{t} $ which has a $85\%$ accuracy (higher than my model at current).

So is there a way I can use this function to improve the accuracy of my classifier. Baring in mind the fact that I never actually gather a value for velocity, I can only ever predict it.

I thought that maybe using lagged variable might work, but since $f$ in decidedly non-linear I am not sure if the model would be able to cope, and it seems slightly lacking in elegance.

My next thought would be to add a feature of $f(x)$ or $f(y)$ but again since $f$ is non linear this may not work.

My last idea would be to use something like the Kalman filter to join both the prediction based on $(x_{t}$,$y_{t})$ and the prediction based on $z_{t-1}$ but I feel this may lead to error propagation.

So my questions are: Does what I am doing have an actual name (i.e what could I type into google to get either papers or other questions about this topic)?

Would any of these approaches work?

Does anyone have any better ideas?

  • $\begingroup$ Could you provide additional information? What do you mean with 'non-time' data in the title of your question? And why are you interested in elegance when it comes to prediction, accuracy is the think you should care about... In general time-series prediction tasks I would consider doing research in the field of state-space models (e.g. Kalman Filter), you could also investigate Granger-Causalities, work with Neural Networks...there is simply too much do you give a precise answer without further specification. $\endgroup$ – muffin1974 Aug 23 '16 at 9:07
  • $\begingroup$ @muffin1974, By non-time data I meant that the variables I am training my models on are not dependent on time (specifically they are readings from an tachometer). And I am using these readings to predict the velocity of a vehicle (the z value). But separate from all of this, I have a function that gives an estimate of the velocity if it is given the previous velocity ($f$). I am trying to come up with a way of combining them without causing error propagation. I never actually gather the value of the velocity I can only ever predict it from x and y $\endgroup$ – Fiery Chicken Aug 23 '16 at 9:57
  • $\begingroup$ How did you come up with the function $f(z_t)$ ? Did you learn it from data? If so, why not fit a full model like $z_t = f(z_{t-1}, x_t, y_t) + \epsilon_t$ where you use something to find the best $f(..)$? $\endgroup$ – horaceT Aug 24 '16 at 21:17
  • $\begingroup$ @horaceT No I didn't learn it from the data although I have checked and the data I have in my training set does abide by the function $\endgroup$ – Fiery Chicken Aug 25 '16 at 5:19
  • $\begingroup$ @FieryChicken. I think the general term for what you're trying to do is NARX. This can also be done with a particular neural network architecture. $\endgroup$ – Alexander F. Aug 25 '16 at 16:17

Have you tried Gaussian Processes?

A random function $f$ is said to be a Gaussian Process if $$ f(x) \sim GP(m(x), k(x, x')), \quad x, x' \in \mathbb R^p. $$

The mean function $m$ is the location parameter and the kernel function represents the covariance between two outputs as function of the inputs: $$ k(x, x') = \mathrm{cov}[f(x), f(x')]. $$

You can encode your prior information about the dependence of $z$ on the previous value in the kernel function. This means that you have to assign a higher correlation value when the points are neighbouring. The Markovian kernel is the Exponential kernel (or Ornstein-Uhlenbeck). The GP will learn itself the nonlinear dependence in the output space.

If you are not familiar with the model, you need to have a look at Kevin Murphy, Machine Learning A Probabilistic Perspective, MIT - chapter 15.

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