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diff --git a/time_series_model.py b/time_series_model.py
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+++ b/time_series_model.py
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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from statsmodels.tsa.arima_model import ARIMA
+import statsmodels.api as sm
+import sqlite3 as sql
+import pmdarima as pm
+from sql_functions import execute_sql_statement
+
+def plot_data(dates):
+    sql_stmt = "select date, cast(avg_temperature as real) as temp from temperature"
+    result = execute_sql_statement(sql_stmt)
+    data = pd.DataFrame(result, columns=["date","temp"])
+    data.set_index('date', inplace=True)
+    # print(data)
+    new_model = pm.auto_arima(data.temp, start_p=1, start_q=1,
+                        test='adf',       # use adftest to find optimal 'd'
+                        max_p=3, max_q=3, # maximum p and q
+                        m=5,              # frequency of series
+                        d=None,           # let model determine 'd'
+                        seasonal=False,   # No Seasonality
+                        start_P=0, 
+                        D=0, 
+                        trace=True,
+                        error_action='ignore',  
+                        suppress_warnings=True, 
+                        stepwise=True)
+
+    # print(new_model.summary())
+
+    # new_model.plot_diagnostics(figsize=(10,8))
+    # # plt.show()
+
+    n_periods =30
+    fc, confint = new_model.predict(n_periods = n_periods, return_conf_int = True)
+    # print(fc)
+
+    n_years = ['1960-12-02', '1960-12-03', '1960-12-04', '1960-12-05', '1960-12-06', '1960-12-07', '1960-12-08', '1960-12-09', '1960-12-10', '1960-12-11', '1960-12-12', '1960-12-13', '1960-12-14', '1960-12-15', '1960-12-16', '1960-12-17', '1960-12-18', '1960-12-19', '1960-12-20', '1960-12-21', '1960-12-22', '1960-12-23', '1960-12-24', '1960-12-25', '1960-12-26', '1960-12-27', '1960-12-28', '1960-12-29', '1960-12-30', '1960-12-31']
+    fc_ind = pd.Series(n_years)
+
+    fc_series = pd.Series(fc, index=fc_ind)
+    lower_series = pd.Series(confint[:, 0], index=fc_ind)
+    upper_series = pd.Series(confint[:, 1], index=fc_ind)
+
+    plt.figure(figsize=(12, 5))
+    # # # plt.plot(np.log10(data.temp))
+    # plt.plot(fc_series, color="darkred")
+    # # # plt.xlabel("Year")
+    # # # plt.ylabel(data. + " Rate")
+    # # plt.fill_between(lower_series.index, 
+    # #                     lower_series, 
+    # #                     upper_series, 
+    # #                     color="k", alpha=.35)
+    # # # plt.xticks(np.arange(min(data.index), max(upper_series.index)+3, 3.0))
+    # # # plt.title("Final Forecast of Crude Death Rate")
+    # # # plt.legend(("past", "forecast", "95% confidence interval"), loc="upper right")
+    plt.show()
+
+
+