forecasting.ipynb - #189 time-series analysis using kats.

.replit

@@ -0,0 +1,2 @@
+language = "python3"
+run = ""

gs_quant/timeseries/__init__.py

@@ -31,5 +31,6 @@
 from .measures_xccy import *
 from .measures_fx_vol import *
 from .helper import *
+from .kats import *
 
 __name__ = 'timeseries'

gs_quant/timeseries/analysis.py

@@ -19,7 +19,6 @@
 from gs_quant.datetime import relative_date_add
 from gs_quant.timeseries.datetime import *
 from .helper import plot_function
-
 """
 Timeseries analysis library contains functions used to analyze properties of timeseries, including laging, differencing,
 autocorrelation, co-integration and other operations
@@ -196,7 +195,9 @@ class LagMode(Enum):
 
 
 @plot_function
-def lag(x: pd.Series, obs: Union[Window, int, str] = 1, mode: LagMode = LagMode.EXTEND) -> pd.Series:
+def lag(x: pd.Series,
+        obs: Union[Window, int, str] = 1,
+        mode: LagMode = LagMode.EXTEND) -> pd.Series:
     """
     Lag timeseries by a number of observations or a relative date.
 
@@ -248,7 +249,8 @@ def lag(x: pd.Series, obs: Union[Window, int, str] = 1, mode: LagMode = LagMode.
     # Determine how we want to handle observations prior to start date
     if mode == LagMode.EXTEND:
         if x.index.resolution != 'day':
-            raise MqValueError(f'unable to extend index with resolution {x.index.resolution}')
+            raise MqValueError(
+                f'unable to extend index with resolution {x.index.resolution}')
         kwargs = {'periods': abs(obs) + 1, 'freq': 'D'}
         if obs > 0:
             kwargs['start'] = x.index[-1]

gs_quant/timeseries/forecasting.ipynb

@@ -0,0 +1,99 @@
+# Copyright 2018 Goldman Sachs.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#  http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+#
+# Marquee Plot Service will attempt to make public functions (not prefixed with _) from this module available.
+# Such functions should be fully documented: docstrings should describe parameters and the return value, and provide
+# a 1-line description. Type annotations should be provided for parameters.
+#
+#Kats (Kits to Analyze Time Series) is a light-weight, easy-to-use,extenable, and generalizable framework to perform time series analysis in Python. Time series analysis is an essential component of data science and engineering work. 
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from kats.consts import TimeSeriesData
+from gs_quant.datetime import relative_date_add
+from gs_quant.timeseries.datetime import *
+from .helper import plot_function
+
+
+
+
+# Note: If the column holding the time values is not called time, you will want to specify the name of this column.
+gs_quant.columns = ["time", "value"]
+ts = TimeSeriesData(gs_quant)
+
+from kats.models.sarima import SARIMAModel, SARIMAParams
+
+
+# create SARIMA param class
+params = SARIMAParams(
+    p = 2, 
+    d=1, 
+    q=1, 
+    trend = 'cti', 
+    seasonal_order=(1,0,1,12)
+    )
+
+# initiate SARIMA model
+m = SARIMAModel(data=ts, params=params)
+
+# fit SARIMA model
+m.fit()
+
+# generate forecast values
+fcst = m.predict(
+    steps=30, 
+    freq="MS"
+    )
+
+# make plot to visualize
+m.plot()
+
+
+# import the param and model classes for Prophet model
+from kats.models.prophet import ProphetModel, ProphetParams
+
+# create a model param instance
+params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results
+
+# create a prophet model instance
+m = ProphetModel(ts, params)
+
+# fit model simply by calling m.fit()
+m.fit()
+
+# make prediction for next 30 month
+fcst = m.predict(steps=30, freq="MS")
+
+# plot to visualize
+m.plot()
+
+from kats.models.holtwinters import HoltWintersParams, HoltWintersModel
+
+
+params = HoltWintersParams(
+            trend="add",
+            #damped=False,
+            seasonal="mul",
+            seasonal_periods=12,
+        )
+m = HoltWintersModel(
+    data=ts, 
+    params=params)
+
+m.fit()
+
+fcst = m.predict(steps=20, alpha = 0.3)
+m.plot()

setup.py

@@ -85,9 +85,14 @@
         "internal": ["gs_quant_internal>=1.1.30", "requests_kerberos"],
         "turbo": ["quant-extensions"],
         "notebook": ["jupyter", "matplotlib~=3.1.0", "seaborn", "treelib"],
-        "test": ["pytest", "pytest-cov", "pytest-mock", "testfixtures", "nbconvert", "nbformat", "jupyter_client"],
-        "develop": ["wheel", "sphinx", "sphinx_rtd_theme", "sphinx_autodoc_typehints", "pytest", "pytest-cov",
-                    "pytest-mock", "testfixtures"]
+        "test": [
+            "pytest", "pytest-cov", "pytest-mock", "testfixtures", "nbconvert",
+            "nbformat", "jupyter_client"
+        ],
+        "develop": [
+            "wheel", "sphinx", "sphinx_rtd_theme", "sphinx_autodoc_typehints",
+            "pytest", "pytest-cov", "pytest-mock", "testfixtures"
+        ]
     },
     classifiers=[
         "Programming Language :: Python :: 3",