Page 176 - Data Science Algorithms in a Week

Time Series Analysis

sales_march = 1.279*(year+2/12) - 2557.778 - 0.464

= 1.279*(2018+2/12) - 2557.778 - 0.464 = 22.993
sales_april = 1.279*(year+3/12) - 2557.778 - 0.608

= 1.279*(2018+3/12) - 2557.778 - 0.608 = 22.956

sales_may = 1.279*(year+4/12) - 2557.778 - 0.165
= 1.279*(2018+4/12) - 2557.778 - 0.165 = 23.505

sales_june = 1.279*(year+5/12) - 2557.778 - 0.321

= 1.279*(2018+5/12) - 2557.778 - 0.321 = 23.456
sales_july = 1.279*(year+6/12) - 2557.778 - 0.003

= 1.279*(2018+6/12) - 2557.778 - 0.003 = 23.881

sales_august = 1.279*(year+7/12) - 2557.778 - 0.322
= 1.279*(2018+7/12) - 2557.778 - 0.322 = 23.668

sales_september = 1.279*(year+8/12) - 2557.778 - 0.116

= 1.279*(2018+8/12) - 2557.778 - 0.116 = 23.981

sales_october = 1.279*(year+9/12) - 2557.778 + 0.090
= 1.279*(2018+9/12) - 2557.778 + 0.090 = 24.293

sales_november = 1.279*(year+10/12) - 2557.778 + 1.833

= 1.279*(2018+10/12) - 2557.778 + 1.833 = 26.143
sales_december = 1.279*(year+11/12) - 2557.778 + 3.552

= 1.279*(2018+11/12) - 2557.778 + 3.552 = 27.968

Conclusion
Therefore, we complete the table with sales for the year 2018 based on the seasonal
equations above.

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