%run ../initscript.py
HTML("""
<div id="popup" style="padding-bottom:5px; display:none;">
    <div>Enter Password:</div>
    <input id="password" type="password"/>
    <button onclick="done()" style="border-radius: 12px;">Submit</button>
</div>
<button onclick="unlock()" style="border-radius: 12px;">Unclock</button>
<a href="#" onclick="code_toggle(this); return false;">show code</a>
""")

Enter Password:

Submit

Unclock show code

%run ../../notebooks/loadtsfuncs.py
%matplotlib inline
toggle()

show code

Case Study

df_drug = pd.read_csv(dataurl+'drug_sales.csv', parse_dates=['date'], index_col='date')
df_drug['year'] = [d.year for d in df_drug.index]
df_drug['month'] = [d.strftime('%b') for d in df_drug.index]

Python code cheat sheet:

Analysis:

df_drug.head()

plot_time_series(df_drug, 'sales', title='Drug Sales')

seasonal_plot(df_drug, ['month','sales'], title='Drug Sales')

boxplot(df_drug, ['month','sales'], title='Drug Sales')

stationarity_test(df_drug.sales, title='Drug Sales')

decomp(df_drug.sales)

detrend(df_drug['sales'])

deseasonalize(df_drug.sales, model, title='Drug Sales')
    #set model = 'multiplicative' or 'additive'#

plot_acf_pacf(df_drug.sales, acf_lag=50, pacf_lag=30)

differencing(df_drug, 'sales', title='Drug Sales')

SARIMA fitting and forecast:

sarima_drug = pm.auto_arima(df_drug.sales, start_p=1, start_q=1,
                         test='adf',
                         max_p=3, max_q=3, m=12,
                         start_P=0, seasonal=True,
                         d=None, D=1, trace=True,
                         error_action='ignore',
                         suppress_warnings=True,
                         stepwise=True)

sarima_drug.summary()

sarima_forcast(sarima_drug, df_drug, 'sales', forecast_periods=24, freq='month')

SARIMAX fitting and forecast:

df_drug = add_seasonal_index(df_drug, 'sales', freq='month', model='multiplicative')
sarimax_drug = pm.auto_arima(df_drug[['sales']], exogenous=df_drug[['seasonal_index']],
                           start_p=1, start_q=1,
                           test='adf',
                           max_p=3, max_q=3, m=12,
                           start_P=0, seasonal=True,
                           d=None, D=1, trace=True,
                           error_action='ignore',
                           suppress_warnings=True,
                           stepwise=True)

sarimax_drug.summary()

sarimax_forcast(sarimax_drug, df_drug, 'sales', forecast_periods=24, freq='month')