Introduction to Time Series Analysis and Forecasting

Introduction to Time Series Analysis and Forecasting

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 .

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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