Time Series support

[TomKealy]

I have quite a few stan files for time series lying around and could contribute a module to this project. `from abc import ABCMeta, abstractstaticmethod from typing import Dict import pystan as ps

class BaseStanData(Dict): def append(self, **kwargs): data = self.copy() data.update(kwargs) return data

class BaseModel(metaclass=ABCMeta): model_code: str

def __init__(self, **kwargs):
    self.kwargs = kwargs

@abstractstaticmethod
def preprocess(dat: BaseStanData) -> BaseStanData:
    pass

class BaseModelResult(metaclass=ABCMeta): pass

class TimeSeriesStanData(BaseStanData): def init(self, y: np.array): super().init() assert len(y.shape) == 1, 'Mismatch dimension. y must be 1 dimensional array'

    self.update(
        {
            'y': y,
            'T': y.shape[0]
        }
    )

class TimeSeriesModel(BaseModel): def init(self, kwargs): super().init(kwargs)

def fit(self, y: np.array):
    return TimeSeriesModelResult(
        self,
        ps.stan(
            model_code=self.model_code,
            data=self.preprocess(
                TimeSeriesStanData(y)
            ),
            **self.kwargs
        )
    )

@staticmethod
def preprocess(dat: TimeSeriesStanData) -> TimeSeriesStanData:
    return dat

class TimeSeriesModelResult(BaseModelResult): def init(self, model: TimeSeriesModel, stanfit): self.model = model self.stanfit = stanfit

def predict(self, y: np.array) -> np.array:
    return self.predict_dist(y)

def check_fit():
    u = self.stanfit.extract(permuted=True)['u'].mean(axis=0)
    v = self.stanfit.extract(permuted=True)['v'].mean(axis=0)
    fit_check = u + v
    return fit_check

class BayesianStructuralTimeSeries(TimeSeriesModel): model_code = """data { int T; vector[T] y; }

    parameters {
      vector[T] u_err; //Slope innovation
      vector[T] v_err; //Level innovation
      real beta;
      real <lower=0> s_obs;
      real <lower=0> s_slope;
      real <lower=0> s_level;
    }

    transformed parameters {
      vector[T] u; //Level
      vector[T] v; //Slope
      u[1] = u_err[1];
      v[1] = v_err[1];
      for (t in 2:T) {
        u[t] = u[t-1] + v[t-1] + s_level * u_err[t];
        v[t] = v[t-1] + s_slope * v_err[t];
      }
    }

    model {
      u_err ~ normal(0,1);
      v_err ~ normal(0,1);
      y ~ normal (u, s_obs);
    }"""

@staticmethod
def preprocess(dat: TimeSeriesStanData) -> TimeSeriesStanData:
    return dat.append(sigma_upper=dat['y'])`

As an example.

I've tried pushing to a new branch but I've been denied permission, can I contribute? I can add AR/MA/ARMA etc.

[sdual]

@TomKealy Think you for opening the issue. Development of scikit-stan has been suspended until recently. I will totally redesign the structure of scikit-stan before long. Can you open the issue again after the redesign and the development of base structure are completed?

[TomKealy]

@sdual Yes, that's not a problem!

[TomKealy]

Hello - has development been restarted?