The Stack Overflow podcast is back! Listen to an interview with our new CEO.

Tag Info

Hot answers tagged time-series

4

Short answer: this should be no problem. I'm pretty sure you'll have somewhat lower power than if you had a perfectly balanced design, but there is no fundamental difficulty. Here's an example where I subsample the (complete/balanced) sleepstudy data set and show that it works fine (and the results don't change very much). The only specific issue that I ...

1

So you are right that the sampling frequency needs to be constant and the same for both signals, according to the literature, https://ieeexplore.ieee.org/document/1163055. You're also right that the signals do not need to be the same length, and the ends do not need to match. I also agree that resampling / interpolating to a smaller sampling frequency could ...

1

As far as I'm aware, you can't do that. One of the primary assumptions of using DTW is that each signal is sampled at a constant sampling rate. The original paper also states that the sampling rate should be common to each other. I can invisage a change to the original code that could be made to account for a different sampling rate in each, however a non-...

1

Rather than criticize R**2 which has been done well elsewhere , the real question is how to measure the adequacy of a model given (N) observations in total for a particular withheld set length (K=5) horizons based upon (W) withheld values ..the prediction interval. For example if we have N=120 historical values and are concerned with an accuracy measure for ...

1

I have observations $\{3,2,1,0\}, n=4$ I count $\overline{X}= \frac{3}{2} \hat{γ_1}(0)=\frac{5}{4}, \hat{γ_1}(3)=-\frac{9}{4}$ then $\widehat {Var}(X_1 + X_4)=\widehat {Var}(X_1)+\widehat {Var}(X_4)+2\widehat {Cov}(X_1, X_4)= 2\hat{γ_1}(0)+2\hat{γ_1}(3)= \frac{5}{2} - \frac{9}{2} = -2<0$

1

No matter how you slice it, this is effectively a different question - instead of looking for a value Y(X), you're asking about the parameters of a distribution with a mean of Y(X) and variance/standard deviation E(X, deltaT). I see two broad approaches here: 1) The simpler implementation is to run your current model on some historical data, tweak the lag, ...

1

If your method is in the forecast class and you have more than 24 observations, you should have no problem in running tsCV with h = 24. It will return a matrix with h = 24 columns and the number of rows equals to your data size. If you only want the cv errors for K=24, then you can do the following: if d is the number of days in your data you, the code ...

1

For reference, let your model be: $$X_t=\phi X_{t-1} + \epsilon_t$$ Say you have data from 1 to $T$. So your point forecast for $X_{T+1}$ would be $$E(X_{T+1}|\{X_t\}_{t=1}^T) = \phi X_T$$ and the confidence interval of $X_{T+1}$ would depend on the distribution you assume for $\epsilon_T$. So forecast follows normal if you assume so for the error term. ...

Only top voted, non community-wiki answers of a minimum length are eligible