Very short time series in unbalanced panel, assert n_windows>0 about esrnn_torch HOT 3 CLOSED

`# Python
import pandas as pd

#get data
data = pd.read_csv("DHS_Daily_Report.csv")

#transforming date variable
data["Date"] = pd.to_datetime(data["Date"], format = "%m/%d/%Y")
data["Date"] = data["Date"].dt.strftime("%Y-%m-%d")

#rename forecasting variable
data = data.rename(columns = {'Total Individuals in Shelter' : 'y'})
data = data.rename(columns = {'Date' : 'ds'})

#selecting variables
#selecting variables
dataset = data.loc[data["ds"] <= "2020-11-11", ["ds","y", "Easter",
                                   "Thanksgiving", "Christmas"]]
future = data.loc[data["ds"] >= "2020-11-12", ["ds","y", "Easter",
                                  "Thanksgiving", "Christmas"]]

#adding unique IDs
dataset.insert(0, 'unique_id', dataset.index)

#isolate Y and X
y_df = dataset.loc[:, ["unique_id", "ds", "y"]]
X_df = dataset.loc[:, ["unique_id", "ds", "Easter"]]
X_df = X_df.rename(columns = {'Easter' : 'x'})

#forecasting model
from ESRNN import ESRNN
model = ESRNN(max_epochs=25, 
              freq_of_test=5, 
              batch_size=4, learning_rate=1e-4,
              per_series_lr_multip=0.8, lr_scheduler_step_size=10,
              lr_decay=0.1, gradient_clipping_threshold=50,
              rnn_weight_decay=0.0, level_variability_penalty=100,
              testing_percentile=50, training_percentile=50,
              ensemble=False, max_periods=25, seasonality=[],
              input_size=4, output_size=6,
              cell_type='LSTM', state_hsize=40,
              dilations=[[1], [6]], add_nl_layer=False,
              random_seed=1, device='cpu')
model.fit(X_df, y_df)