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 .

In this article we are going to discuss about the results and the theory behind them based on ‘Predict Future Sales’ data set .

Note: to know every single details and detailed theory behind it please check this tutorial .

Introduction to Time Series Analysis and Forecasting - I

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and…

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Data set Description:

we have:

1. date — every date of items sold
2. date_block_num — this number given to every month
3. shop_id — unique number of every shop
4. item_id — unique number of every item
5. item_price — price of every item
6. item_cnt_day — number of items sold on a particular day

Packages we need:

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:

df=pd.read_csv('sales_train.csv')

df.head()

Data types:

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)

Visualizing the time series data:

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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