Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values .
Note: to know every single details and detailed theory behind it please check this tutorial .
import warnings import itertools import numpy as np import matplotlib.pyplot as plt warnings.filterwarnings("ignore") plt.style.use('fivethirtyeight') import pandas as pd import statsmodels.api as sm from statsmodels.tsa.arima_model import ARIMA from pandas.plotting import autocorrelation_plot from statsmodels.tsa.stattools import adfuller, acf, pacf,arma_order_select_ic import matplotlibmatplotlib.rcParams['axes.labelsize'] = 14 matplotlib.rcParams['xtick.labelsize'] = 12 matplotlib.rcParams['ytick.labelsize'] = 12 matplotlib.rcParams['text.color'] = 'k'
read the data:
date object date_block_num int64 shop_id int64 item_id int64 item_price float64 item_cnt_day float64 dtype: object
Now we have to convert “date” object to string (YYYY-MM-DD)
import datetime df['date']=pd.to_datetime(df.date)
ts=df.groupby(["date_block_num"])["item_cnt_day"].sum() ts.astype('float') plt.figure(figsize=(16,8)) plt.title('Total Sales of the company') plt.xlabel('Time') plt.ylabel('Sales') plt.plot(ts)
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