diff --git a/Analytics/timeseries.py b/Analytics/timeseries.py
new file mode 100644
index 0000000..ebcd5a5
--- /dev/null
+++ b/Analytics/timeseries.py
@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+
+# Commented out IPython magic to ensure Python compatibility.
+# %pip install pandas_datareader --upgrade
+
+from datetime import datetime, date
+from pandas_datareader import data
+import matplotlib.pyplot as plt
+import pandas as pd
+import os
+import warnings
+warnings.filterwarnings('ignore')
+import numpy as np
+from pylab import rcParams
+rcParams['figure.figsize'] = 10, 2
+from statsmodels.tsa.arima_model import ARIMA
+import math
+
+spxTicker = '^SPX'
+start_date = date(1990, 1, 1)
+end_date = date.today()
+
+panel_data = data.DataReader('^SPX', 'stooq',start_date,end_date)
+
+panel_data
+
+close_SPY = pd.DataFrame(index=panel_data.index, data=panel_data['Close'])
+
+close_SPY.head()
+
+close = close_SPY['Close']
+
+short_rolling_close = close.rolling(window=20).mean()
+long_rolling_close = close.rolling(window=100).mean()
+
+fig, ax = plt.subplots(figsize=(16,9))
+
+ax.plot(close.index, close, label='S&P500')
+ax.plot(short_rolling_close.index, short_rolling_close, label='20 days rolling')
+ax.plot(long_rolling_close.index, long_rolling_close, label='100 days rolling')
+
+ax.set_xlabel('Date')
+ax.set_ylabel('Adjusted closing price ($)')
+ax.legend()
+
+
+df_log = np.log(close)
+def arima_model(p,d,q):
+  if (d > 2):
+    print("d cannot be more than 2!")
+    return None
+  elif (p == 1 and d == 1 and q == 1):
+    print("p,d & q cannot be all 1!")
+    return None
+  train_data, test_data = df_log[:int(len(df_log)*0.85)], df_log[int(len(df_log)*0.85):]
+  model = ARIMA(df_log, order=(p, d, q))
+  fitted = model.fit(disp=-1)
+  fc, se, conf = fitted.forecast(len(test_data), alpha=0.05)
+  future_index = pd.date_range(start = date.today(),periods= len(test_data))
+  fc_series = pd.Series(fc, index=future_index)
+  lower_series = pd.Series(conf[:, 0], index=future_index)
+  upper_series = pd.Series(conf[:, 1], index=future_index)
+  # Plot
+  plt.figure(figsize=(16,9), dpi=72)
+  plt.plot(np.exp(df_log), label='training')
+  plt.plot(np.exp(fc_series), color = 'orange',label='Future Predicted Stock Price')
+  plt.fill_between(lower_series.index, np.exp(lower_series), np.exp(upper_series), 
+                      color='k', alpha=.10)
+  plt.title('S & P 500 Index Price Prediction')
+  plt.xlabel('Time')
+  plt.ylabel('Price')
+  plt.legend(loc='upper left', fontsize=8)
+  plt.legend(loc='upper left', fontsize=8)
+  fname = "SNP"+"-"+str(date.today())+"-("+str(p)+","+str(d)+","+str(q)+")"+'-predictions'
+  plt.savefig(fname)
+
+arima_model(3,2,1)