# Tag Info

### Alternatives to Vector Autoregression

I'm a bit late to this party but depending on what you're trying to do, an OLS with time series errors model, or Vector Error Correction Models (VECMs) could work as an alternative to VAR if you're ...

### GLMM with time-series covariance and binary response variable?

You can do this in the {mvgam} package, which fits dynamic regression models in State Space form for a fairly wide range of observation families. The ...

### Time series forecasting - Residuals not white noise

ARIMA is a linear model. It might be possible to explain some of these unusual variations in residuals by using non-linear models. I would have tried Machine Learning models on residuals to identify ...
1 vote

### How to measure the strength of relationship

It is not obvious what is meant by "approx normal time series" in your question. Judging from the rest of it, it seems you meant that both your time series $x_t$ and $y_t$ follow a white ...
• 45

### Time series and separating variance by time scale

This sounds like a good candidate for a State-Space model, where you simultaneously model the underlying dynamics of the true process as well as the observation process. In State-Space models, both of ...

### Ecological modelling: multivariate abundance time-series data

For modeling integer-valued time series (or any non-Gaussian time series), I would opt for a State-Space model that allows the latent dynamic process to evolve independently of the observations. This ...

### How to forecast integer time series in R?

For modeling and forecasting integer-valued time series (or any non-Gaussian time series), I would opt for a State-Space model that allows the latent dynamic process to evolve independently of the ...

### Time Series of Wound Healing percentages / proportions

I agree this one is challenging, but you can design dynamic models that will respect the proportional nature and the temporal dependence of your observations. The {...

### Time series for count data, with counts < 20

For modeling integer-valued time series (or any non-Gaussian time series), I would opt for a State-Space model that allows the latent dynamic process to evolve independently of the observations. This ...

### How to fit a simple count time series INAR(1) model

For modeling integer-valued time series (or any non-Gaussian time series), I would opt for a State-Space model that allows the latent dynamic process to evolve independently of the observations. This ...
1 vote
Accepted

### How to split and sample "Panel Data" when training a Logistic Regression to predict future outcomes

Best Practice for Splitting Panel Data For splitting panel data, the following approaches are often considered best practices: Temporal Validation (Out-of-Time) This involves setting aside data from ...

### Long-term variance of AR(p)

UPDATE: I think I've figured it out. The Yule-Walker equations are the correct ones. When I was opening the expression of variance I was considering that $V[A+B] = V[A] + V[B]$. I was forgetting the ...
Accepted

### Inferences with Filtered and Smoothed state estimates from a tracking problem

Ok so in my case the only solution I could come up with involved taking samples from the full posterior smoothing distribution. I'm not sure on the correct terminology here (this may be called Gibbs ...
Accepted

### Compute R Squared by Fixing Betas for Multi Linear Regression without Intercept results in a large R Square

In OLS linear regression with an intercept, there are multiple equivalent expressions of $R^2$. Four come to mind. Squared Pearson correlation between the feature and the outcome Squared Pearson ...
• 63.7k

### Distribution of medians of triplicate samples taken from Gaussian distribution

Suppose $X_1, X_2, X_3 \text{ i.i.d. } \sim N(0, 1)$, then the density of the median $M := X_{(2)}$ is given by: \begin{align*} f_M(x) = 6\Phi(x)(1 - \Phi(x))\phi(x), \; x \in \mathbb{R}, \tag{1}\...
• 19.9k

### Distribution of medians of triplicate samples taken from Gaussian distribution

This must be known somewhere because Mathematica (and likely MATLAB and Maple, too) solves this easily. ...
• 3,889

### How to split and sample "Panel Data" when training a Logistic Regression to predict future outcomes

"There seems to be no consensus on what best practice is for splitting/sampling panel data". This is due to the fact that you may group these data in very different ways (e.g. by customer, ...
• 1,343
1 vote

### Regarding explosive AR processes and stationarity

changing an explosive AR process to be in a future dependent form is pointless, real life does not operate that way Is there a reason we don't consider the case of $|\phi|>1$? Is it also because &...
• 73

### Brockwell/Davis seem to say more persistence implies better predictability---do I have a counterexample?

As things stand, I cannot see how the prediction MSE for any process that regularly has an error term $\epsilon_t$ with variance $\sigma^2$ coming in, independently of what happened before, can ever ...
• 24.8k

### timeseries model choose LSTM VS ARIMA

ARIMA forecasts numerical values. You could use it to forecast the student's future grades separately (and it would likely not work very well), but it will not help you in classifying a student as to ...
• 125k

### Forecasting positive time series with missing values and few observations

I would suggest you start with a State-Space model that allows for latent temporal processes to evolve through time, irrespective of whether or not observations occurred in that timepoint. This kind ...
Accepted

### How to detect a plateau at the end of a short time series?

I would use a Generalized Additive Model (GAM) to tackle this question. The penalized splines fitted with GAMs will be less likely to overfit than your LOESS algorithm, and you can use hierarchical ...

### Visualising complex data with various groups + sub-groups over time period

A tangent suggestion since this thread has come to the top... Ordering groups alphabetically is rarely meaningful for plotting. If a better ordering doesn't exist, as may be the case for names of ...
• 4,549
1 vote
Accepted

### Synthetic Control - difference in data regarding the frequency

I am going to interpret "best" in terms of using the level of aggregation for the predictors that optimizes your ability to track the trajectory of the outcome variable for the treated unit ...
• 35.9k

### Are stationary processes non-predictable, and non-stationary ones predictable?

Here is a possible angle on this (cf. Brockwell/Davis, p40): The best linear predictor $l(Y_{T})=aY_{T}+b$ for a stationary time series $Y_{T+h}$ based on $Y_{T}$ minimizes $E[Y_{T+h}-aY_T-b]^2$ and ...
• 33.6k
1 vote

### Different time trends of groups: quadratic vs. linear decline

If you know/want to assume a priori that group A and group B follow time trends with different functional forms, you can hack it by using a dummy variable/indicator variable for each group. You can ...
• 44k

### Mixed model: Which parameters to provide for sample size calculations?

It seems you are trying to achieve three things here: Fit a multilevel model. Employ multiple DVs in said model. Estimate the statistical power of this model. Sointu's points are indeed useful for ...
• 14.6k

### Mixed model: Which parameters to provide for sample size calculations?

Yeah, the simr package is very good for power analysis for a multilevel model. Generally, you generate a mock dataset with your ID and time variables and all ...
• 2,258
1 vote

• 2,507