Finished Functions

python/smooth/adam_general/core/utils/dump.py

@@ -0,0 +1,71 @@
+# estimator commented out lines 2754 to 2821
+adam_created_arima = filler(
+            b_values['B'],
+            ets_model, e_type, t_type, s_type, model_is_trendy, model_is_seasonal,
+            components_number_ets, components_number_ets_non_seasonal,
+            components_number_ets_seasonal, components_number_arima,
+            lags, lags_model, lags_model_max,
+            adam_created['mat_vt'], adam_created['mat_wt'], adam_created['mat_f'], adam_created['vec_g'],
+            persistence_estimate, persistence_level_estimate, persistence_trend_estimate,
+            persistence_seasonal_estimate, persistence_xreg_estimate,
+            phi_estimate,
+            initial_type, initial_estimate,
+            initial_level_estimate, initial_trend_estimate, initial_seasonal_estimate,
+            initial_arima_estimate, initial_xreg_estimate,
+            arima_model, ar_estimate, ma_estimate, ar_orders, i_orders, ma_orders,
+            ar_required, ma_required, arma_parameters,
+            non_zero_ari, non_zero_ma, adam_created['arima_polynomials'],
+            xreg_model, xreg_number,
+            xreg_parameters_missing, xreg_parameters_included,
+            xreg_parameters_estimated, xreg_parameters_persistence, constant_estimate
+        )
+
+        # Write down the initials in the recent profile
+        profiles_recent_table[:] = adam_created_arima['mat_vt'][:, :lags_model_max]
+
+        # Do initial fit to get the state values from the backcasting
+        adam_fitted = adam_fitter_wrap(
+            adam_created_arima['mat_vt'], adam_created_arima['mat_wt'], adam_created_arima['mat_f'], adam_created_arima['vec_g'],
+            lags_model_all, index_lookup_table, profiles_recent_table,
+            e_type, t_type, s_type, components_number_ets, components_number_ets_seasonal,
+            components_number_arima, xreg_number, constant_required,
+            y_in_sample, ot, True
+        )
+
+        adam_created['mat_vt'][:, :lags_model_max] = adam_fitted['mat_vt'][:, :lags_model_max]
+        # Produce new initials
+        b_values_new = initialiser(
+            ets_model, e_type, t_type, s_type, model_is_trendy, model_is_seasonal,
+            components_number_ets_non_seasonal, components_number_ets_seasonal, components_number_ets,
+            lags, lags_model, lags_model_seasonal, lags_model_arima, lags_model_max,
+            adam_created['mat_vt'],
+            persistence_estimate, persistence_level_estimate, persistence_trend_estimate,
+            persistence_seasonal_estimate, persistence_xreg_estimate,
+            phi_estimate, initial_type, initial_estimate,
+            initial_level_estimate, initial_trend_estimate, initial_seasonal_estimate,
+            initial_arima_estimate, initial_xreg_estimate,
+            arima_model, ar_required, ma_required, ar_estimate, ma_estimate, ar_orders, ma_orders,
+            components_number_arima, components_names_arima, initial_arima_number,
+            xreg_model, xreg_number,
+            xreg_parameters_estimated, xreg_parameters_persistence,
+            constant_estimate, constant_name, other_parameter_estimate
+        )
+        B = b_values_new['B']
+        # Failsafe, just in case if the initial values contain NA / NaN
+        B[np.isnan(B)] = b_values['B'][np.isnan(B)]
+
+
+
+        # Fix for mixed ETS models producing negative values
+        if (e_type == "M" and any(t in ["A", "Ad"] for t in [t_type, s_type]) or
+            t_type == "M" and any(t in ["A", "Ad"] for t in [e_type, s_type]) or
+            s_type == "M" and any(t in ["A", "Ad"] for t in [e_type, t_type])):
+            if e_type == "M" and ("level" in B) and (B["level"] <= 0):
+                B["level"] = y_in_sample[0]
+            if t_type == "M" and ("trend" in B) and (B["trend"] <= 0):
+                B["trend"] = 1
+            seasonal_params = [p for p in B.keys() if p.startswith("seasonal")]
+            if s_type == "M" and any(B[p] <= 0 for p in seasonal_params):
+                for p in seasonal_params:
+                    if B[p] <= 0:
+                        B[p] = 1

python/smooth/adam_general/core/utils/ic.py

@@ -114,7 +114,7 @@ def BICc(loglik, nobs=None, df=None):
     bic = BIC(loglik, nobs, df)
     return bic + (np.log(nobs) * df * (df + 1)) / (nobs - df - 1)
 
-def ic_function(ic_name):
+def ic_function(ic_name, loglik):
     """
     Select information criterion function based on name
 
@@ -128,11 +128,14 @@ def ic_function(ic_name):
     function
         Selected information criterion function
     """
+    value = loglik['value']
+    nobs = loglik['nobs']
+    df = loglik['df']
     ic_functions = {
-        'AIC': AIC,
-        'AICc': AICc,
-        'BIC': BIC,
-        'BICc': BICc
+        'AIC': AIC(value, nobs, df),
+        'AICc': AICc(value, nobs, df),
+        'BIC': BIC(value, nobs, df),
+        'BICc': BICc(value, nobs, df)
     }
 
     if ic_name not in ic_functions:

python/smooth/adam_general/core/utils/ic.pyZone.Identifier

@@ -0,0 +1,2 @@
+[ZoneTransfer]
+ZoneId=3

python/smooth/adam_general/core/utils/polynomials.py

@@ -0,0 +1,8 @@
+def adam_polynomialiser(parameters, ar_orders, i_orders, ma_orders,
+                       ar_estimate, ma_estimate, arma_parameters, lags):
+    """
+    Creates polynomials for ARIMA models.
+    """
+    # Implementation of adam_polynomialiser goes here
+    # You'll need to move this function from wherever it's currently defined
+    pass