ARIMA Time Series Forecasting and Visualization in Python

In this data science project, we will look at few examples where we can apply various time series forecasting techniques.


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What will you learn

  • Understanding the problem statement

  • Importing the dataset from amazon AWS

  • Single period forecasting and multiple period forecasting

  • Univariate time series forecasting and multivariate time series forecasting

  • Seasonality, Trend, and Cyclicity

  • Moving average as a method of smoothing out the dataset

  • Performing basic EDA

  • Visualizing a time series using graph

  • Timestamping the necessary columns

  • How to decompose a time series

  • Rolling mean or moving average

  • Standardization and Normalization of the time series

  • Visualizing standardized data using barplot

  • What is upsampling and downsampling

  • Understanding the relationship between different variables using Univariate and Multivariate analusis

  • AR, MA, ARIMA, ARMA, UCM and Exponential smoothing model

  • How to implement ARIMA model

  • Defining the evaluation metrics

  • Plotting the error terms or residuals

  • Making predictions using the ARIMA model

  • Plotting the model results

  • Different methods to make the predictions better

Project Description

Time series forecasting has been one of the important area in data science, it is important to predict a variable associated with time elements such as sales, demand, revenue, profit etc. For logistic and supply chain companies, they need to know the exact inventory they need to stock for that they need to predict the demand for future. 

Similarly, people in sales and marketing need to know how much order the customers are going to place so that they can manage their staff. Telecom companies should know how much manpower they need to prepare so that they can handle peak hour traffic etc. In various businesses, at least 5-10 areas where the variable of interest is associated with the time element. 

Let’s look at few examples where we can apply various time series forecasting techniques.

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Curriculum For This Mini Project

02h 35m
02h 27m