diff --git a/python/prophet/serialize.py b/python/prophet/serialize.py
index 71e2300..85140ee 100644
--- a/python/prophet/serialize.py
+++ b/python/prophet/serialize.py
@@ -38,6 +38,73 @@ NP_ARRAY = ['changepoints_t']
 ORDEREDDICT = ['seasonalities', 'extra_regressors']
 
 
+def model_to_dict(model):
+    """Convert a Prophet model to a dictionary suitable for JSON serialization.
+
+    Model must be fitted. Skips Stan objects that are not needed for predict.
+
+    Can be reversed with model_from_dict.
+
+    Parameters
+    ----------
+    model: Prophet model object.
+
+    Returns
+    -------
+    dict that can be used to serialize a Prophet model as JSON or loaded back
+    into a Prophet model.
+    """
+    if model.history is None:
+        raise ValueError(
+            "This can only be used to serialize models that have already been fit."
+        )
+
+    model_dict = {
+        attribute: getattr(model, attribute) for attribute in SIMPLE_ATTRIBUTES
+    }
+    # Handle attributes of non-core types
+    for attribute in PD_SERIES:
+        if getattr(model, attribute) is None:
+            model_dict[attribute] = None
+        else:
+            model_dict[attribute] = getattr(model, attribute).to_json(
+                orient='split', date_format='iso'
+            )
+    for attribute in PD_TIMESTAMP:
+        model_dict[attribute] = getattr(model, attribute).timestamp()
+    for attribute in PD_TIMEDELTA:
+        model_dict[attribute] = getattr(model, attribute).total_seconds()
+    for attribute in PD_DATAFRAME:
+        if getattr(model, attribute) is None:
+            model_dict[attribute] = None
+        else:
+            model_dict[attribute] = getattr(model, attribute).to_json(orient='table', index=False)
+    for attribute in NP_ARRAY:
+        model_dict[attribute] = getattr(model, attribute).tolist()
+    for attribute in ORDEREDDICT:
+        model_dict[attribute] = [
+            list(getattr(model, attribute).keys()),
+            getattr(model, attribute),
+        ]
+    # Other attributes with special handling
+    # fit_kwargs -> Transform any numpy types before serializing.
+    # They do not need to be transformed back on deserializing.
+    fit_kwargs = deepcopy(model.fit_kwargs)
+    if 'init' in fit_kwargs:
+        for k, v in fit_kwargs['init'].items():
+            if isinstance(v, np.ndarray):
+                fit_kwargs['init'][k] = v.tolist()
+            elif isinstance(v, np.floating):
+                fit_kwargs['init'][k] = float(v)
+    model_dict['fit_kwargs'] = fit_kwargs
+
+    # Params (Dict[str, np.ndarray])
+    model_dict['params'] = {k: v.tolist() for k, v in model.params.items()}
+    # Attributes that are skipped: stan_fit, stan_backend
+    model_dict['__prophet_version'] = __version__
+    return model_dict
+
+
 def model_to_json(model):
     """Serialize a Prophet model to json string.
 
@@ -53,57 +120,70 @@ def model_to_json(model):
     -------
     json string that can be deserialized into a Prophet model.
     """
-    if model.history is None:
-        raise ValueError(
-            "This can only be used to serialize models that have already been fit."
-        )
-
-    model_json = {
-        attribute: getattr(model, attribute) for attribute in SIMPLE_ATTRIBUTES
-    }
-    # Handle attributes of non-core types
-    for attribute in PD_SERIES:
-        if getattr(model, attribute) is None:
-            model_json[attribute] = None
-        else:
-            model_json[attribute] = getattr(model, attribute).to_json(
-                orient='split', date_format='iso'
-            )
-    for attribute in PD_TIMESTAMP:
-        model_json[attribute] = getattr(model, attribute).timestamp()
-    for attribute in PD_TIMEDELTA:
-        model_json[attribute] = getattr(model, attribute).total_seconds()
-    for attribute in PD_DATAFRAME:
-        if getattr(model, attribute) is None:
-            model_json[attribute] = None
-        else:
-            model_json[attribute] = getattr(model, attribute).to_json(orient='table', index=False)
-    for attribute in NP_ARRAY:
-        model_json[attribute] = getattr(model, attribute).tolist()
-    for attribute in ORDEREDDICT:
-        model_json[attribute] = [
-            list(getattr(model, attribute).keys()),
-            getattr(model, attribute),
-        ]
-    # Other attributes with special handling
-    # fit_kwargs -> Transform any numpy types before serializing.
-    # They do not need to be transformed back on deserializing.
-    fit_kwargs = deepcopy(model.fit_kwargs)
-    if 'init' in fit_kwargs:
-        for k, v in fit_kwargs['init'].items():
-            if isinstance(v, np.ndarray):
-                fit_kwargs['init'][k] = v.tolist()
-            elif isinstance(v, np.floating):
-                fit_kwargs['init'][k] = float(v)
-    model_json['fit_kwargs'] = fit_kwargs
-
-    # Params (Dict[str, np.ndarray])
-    model_json['params'] = {k: v.tolist() for k, v in model.params.items()}
-    # Attributes that are skipped: stan_fit, stan_backend
-    model_json['__prophet_version'] = __version__
+    model_json = model_to_dict(model)
     return json.dumps(model_json)
 
 
+def model_from_dict(model_dict):
+    """Recreate a Prophet model from a dictionary.
+
+    Recreates models that were converted with model_to_dict.
+
+    Parameters
+    ----------
+    model_dict: Dictionary containing model, created with model_to_dict.
+
+    Returns
+    -------
+    Prophet model.
+    """
+    model = Prophet()  # We will overwrite all attributes set in init anyway
+    # Simple types
+    for attribute in SIMPLE_ATTRIBUTES:
+        setattr(model, attribute, model_dict[attribute])
+    for attribute in PD_SERIES:
+        if model_dict[attribute] is None:
+            setattr(model, attribute, None)
+        else:
+            s = pd.read_json(model_dict[attribute], typ='series', orient='split')
+            if s.name == 'ds':
+                if len(s) == 0:
+                    s = pd.to_datetime(s)
+                s = s.dt.tz_localize(None)
+            setattr(model, attribute, s)
+    for attribute in PD_TIMESTAMP:
+        setattr(model, attribute, pd.Timestamp.utcfromtimestamp(model_dict[attribute]))
+    for attribute in PD_TIMEDELTA:
+        setattr(model, attribute, pd.Timedelta(seconds=model_dict[attribute]))
+    for attribute in PD_DATAFRAME:
+        if model_dict[attribute] is None:
+            setattr(model, attribute, None)
+        else:
+            df = pd.read_json(model_dict[attribute], typ='frame', orient='table', convert_dates=['ds'])
+            if attribute == 'train_component_cols':
+                # Special handling because of named index column
+                df.columns.name = 'component'
+                df.index.name = 'col'
+            setattr(model, attribute, df)
+    for attribute in NP_ARRAY:
+        setattr(model, attribute, np.array(model_dict[attribute]))
+    for attribute in ORDEREDDICT:
+        key_list, unordered_dict = model_dict[attribute]
+        od = OrderedDict()
+        for key in key_list:
+            od[key] = unordered_dict[key]
+        setattr(model, attribute, od)
+    # Other attributes with special handling
+    # fit_kwargs
+    model.fit_kwargs = model_dict['fit_kwargs']
+    # Params (Dict[str, np.ndarray])
+    model.params = {k: np.array(v) for k, v in model_dict['params'].items()}
+    # Skipped attributes
+    model.stan_backend = None
+    model.stan_fit = None
+    return model
+
+
 def model_from_json(model_json):
     """Deserialize a Prophet model from json string.
 
@@ -117,49 +197,5 @@ def model_from_json(model_json):
     -------
     Prophet model.
     """
-    attr_dict = json.loads(model_json)
-    model = Prophet()  # We will overwrite all attributes set in init anyway
-    # Simple types
-    for attribute in SIMPLE_ATTRIBUTES:
-        setattr(model, attribute, attr_dict[attribute])
-    for attribute in PD_SERIES:
-        if attr_dict[attribute] is None:
-            setattr(model, attribute, None)
-        else:
-            s = pd.read_json(attr_dict[attribute], typ='series', orient='split')
-            if s.name == 'ds':
-                if len(s) == 0:
-                    s = pd.to_datetime(s)
-                s = s.dt.tz_localize(None)
-            setattr(model, attribute, s)
-    for attribute in PD_TIMESTAMP:
-        setattr(model, attribute, pd.Timestamp.utcfromtimestamp(attr_dict[attribute]))
-    for attribute in PD_TIMEDELTA:
-        setattr(model, attribute, pd.Timedelta(seconds=attr_dict[attribute]))
-    for attribute in PD_DATAFRAME:
-        if attr_dict[attribute] is None:
-            setattr(model, attribute, None)
-        else:
-            df = pd.read_json(attr_dict[attribute], typ='frame', orient='table', convert_dates=['ds'])
-            if attribute == 'train_component_cols':
-                # Special handling because of named index column
-                df.columns.name = 'component'
-                df.index.name = 'col'
-            setattr(model, attribute, df)
-    for attribute in NP_ARRAY:
-        setattr(model, attribute, np.array(attr_dict[attribute]))
-    for attribute in ORDEREDDICT:
-        key_list, unordered_dict = attr_dict[attribute]
-        od = OrderedDict()
-        for key in key_list:
-            od[key] = unordered_dict[key]
-        setattr(model, attribute, od)
-    # Other attributes with special handling
-    # fit_kwargs
-    model.fit_kwargs = attr_dict['fit_kwargs']
-    # Params (Dict[str, np.ndarray])
-    model.params = {k: np.array(v) for k, v in attr_dict['params'].items()}
-    # Skipped attributes
-    model.stan_backend = None
-    model.stan_fit = None
-    return model
+    model_dict = json.loads(model_json)
+    return model_from_dict(model_dict)