commit-live-students / time_series_101_project

Predicting monthly sales of champagne by Makridakis and Wheelwright, 1989 using Time Series Forecasting

Welcome to the Time-Series Project! In this project, you will demonstrate what you have learned in this course by Predicting the monthly sales of champagne

We have seen in the in-class session how Time Series Forecasting Problem needs to be approached. We learnt about:

• Time Series Analysis and Time Series Forecasting
• Different Kinds of Forecasting
• Visualizing Time-Series Data
• Decomposing Time-Series Data into Trend, Seasonality and residuals
• Time-Series validation and Cross-Validation Strategies
• Measuring Forecasting Errors
• Simple Moving Averages
• Exponential Moving Averages
• Holt's Linear method
• Holt's Winter Method
• AR, MA , ARMA, ARIMA and SARIMA models

Load the required libraries

You can use pandas, datetime, math, sklearn, matplotlib, seaborn and statsmodels for this exercise

Dataset

For this exercise, we will use the sales data of Makridakis and Wheelwright company.

• The dataset provides the number of monthly sales of champagne from January 1964 to September 1972, or just under 10 years of data.
• The values are a count of millions of sales and there are 105 observations.
• The dataset is credited to Makridakis and Wheelwright, 1989.

Why solve this assignment?

Solving this assignment would help you :-

• Visualize Time-Series data effectively

• Build models using Data-Driven approaches on Time-Series data

• For the assignment we will be using the following below packages:

  • pandas
  • datetime
  • math
  • sklearn
  • matplotlib
  • seaborn
  • statsmodels

By completing this project you have an opportunity to win 250 points!

Let's get started!