3 files changed, 57 insertions, 10 deletions
diff --git a/__pycache__/app.cpython-38.pyc b/__pycache__/app.cpython-38.pyc
new file mode 100644
index 0000000..205b647
--- /dev/null
+++ b/__pycache__/app.cpython-38.pyc
diff --git a/dashboard/time_serie_model.py b/dashboard/time_serie_model.py
deleted file mode 100644
index 187d788..0000000
--- a/dashboard/time_serie_model.py
+++ /dev/null
@@ -1,10 +0,0 @@
-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
-
-
-
-
diff --git a/time_serie_model.py b/time_serie_model.py
index 7db8505..1e05543 100644
--- a/time_serie_model.py
+++ b/time_serie_model.py
@@ -3,5 +3,62 @@ 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
+from app import execute_sql_statement
+import sqlite3 as sql
+import pmdarima as pm
+
+conn = sql.connect("database.db")
+
+sql_stmt = "select date, cast(avg_temperature as real) as temp from temperature limit 100"
+
+result = execute_sql_statement(sql_stmt, conn)
+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 = 10
+fc, confint = new_model.predict(n_periods = n_periods, return_conf_int = True)
+print(fc)
+
+n_years = ['1958-05-01', '1958-06-01','1958-07-01','1958-08-01','1958-09-01','1958-10-01','1958-11-01','1958-12-01','1959-01-01','1959-02-01']
+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()
+