SANDYA VB- TIME SERIES FORECASTING PROJECT 2023& study guide with complete solution

Problem: For this particular assignment, the data of different types of wine sales in the 20th century is to be analysed. Both of these data are from the same company but of different wines. As an analyst in the ABC Estate Wines, you are tasked to analyse and forecast Wine Sales in the 20th century. Dataset - R In [1]: import numpy as np import pandas as pd import seaborn as sns from matplotlib import pyplot as plt from import rcParams rcParams['ze'] = 13, 6 1. Read the data as an appropriate Time Series data and plot the data. In [2]: df = _csv("R") In [3]: () Out[3]: YearMonth Rose Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [4]: () In [5]: (); () YearMonth Rose .0 .0 .0 .0 .0 Out[4]: YearMonth Rose .0 .0 .0 .0 .0 Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [6]: date = _range(start='01/01/1980', end='08/01/1995', freq='M') date In [7]: df['Time_Stamp'] = pd.DataFrame(date,columns=['Month']) In [8]: () Out[6]: DatetimeIndex(['', '', '', '', '', '', '', '', '', '', ... '', '', '', '', '', '', '', '', '', ''], dtype='datetime64[ns]', length=187, freq='M') Out[8]: YearMonth Rose Time_Stamp .0 .0 Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [9]: df['Time_Stamp']=_datetime(df['Time_Stamp']) df= _index('Time_Stamp') (['YearMonth'],axis=1, inplace = True) () In [10]: (); () YearMonth Rose Time_Stamp .0 .0 .0 Out[9]: Rose Time_Stamp 112.0 118.0 129.0 99.0 116.0 Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD 2. Perform appropriate Exploratory Data Analysis to understand the data and also perform decomposition. In [11]: ibe(include='all').T In [12]: l().sum() In [13]: Out[11]: count mean std min 25% 50% 75% max Rose 185.0 90. 39. 28.0 63.0 86.0 112.0 267.0 Out[12]: Rose 2 dtype: int64 Out[13]: (187, 1) Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [14]: () In [15]: _, ax = ots(figsize=(30,10)) #yearly plot ot(x = ,y = s[:,0],ax=ax) l('Yearly Rose Wine Sale'); l('Year'); (); In [16]: _, ax = ots(figsize=(22,8)) ot(x = _name(),y = s[:,0],ax=ax) l('Monthly Rose Wine Sale'); l('Months'); (); class '.DataFrame' DatetimeIndex: 187 entries, to Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Rose 185 non-null float64 dtypes: float64(1) memory usage: 2.9 KB Create PDF in your applications with the Pdfcrowd HTML to PDF API PDFCROWD In [17]: from ots import month_plot fig, ax = ots(figsize=(22,8)) month_plot(df,ylabel='Rose Wine Production',ax=ax) (