I have a small sample size time series with potentially lagged predictor values which are also time series. What could be potential methods to analyse these data?

• stat_rethinking_2020

  8 651 2.6 R

  Statistical Rethinking Course Winter 2020/2021

If you want a full course on Bayesian Multilevel models, there's the excellent "statistical rethinking": lectures/content here and code here

• stan

  44 2,521 9.4 C++

  Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

  

Otherwise, you can just put a Gaussian prior centered on zero and you're good to go. It provides less information, but it's still better than the implicit uniform prior of Frequentist methods that assume minus 5 trillion is as likely as 2 for the growth rate of a chick. Prior Predictive Checks (aka Posterior Predictive Checks on priors only, before they are updated by the data) will allow you to visualize if the combination of priors you provided generates realistic data, so it's easy to adjust them. For more info, you can check this and this.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• brms

  9 1,231 9.2 R

  brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

Anyway, I found I can include weights into the brm function by using gr(RE, by = var) to deal with the heterogeneous variance and it should automatically assume that each observation within a group is correlated according to the brms reference manual.

Suggest a related project