Table of Contents

• 1.Introduction to Time Series
• 2.What is Time Series?
• 3.What is not a Time Series
• 4.Where we can find the Time Series Data?
• 5.Features of the Time Series Data
• 6.Time Series Assumptions
• 7.Time Series Types
• 8.Reading and Saving Time Series Objects in Python
• 9.Components of the Time Series
• 10.Decomposition of Time Series
• 11.Moving average forecast
• 12.Handling Missing Values
• 13.Time Series Range, Accuracy and Various Requirements
• 14.ETS Models
  • 14.1 SES, Holt & Holt-Winter Model
    • 14.1.1 SES - ETS(A, N, N) - Simple smoothing with additive errors
    • 14.1.2 Holt - ETS(A, A, N) - Holt's linear method with additive errors
    • 14.1.3 Holt-Winters - ETS(A, A, A) - Holt Winter's linear method with additive errors
    • 14.1.4 Holt-Winters - ETS(A, A, M) - Holt Winter's linear method
  • 14.2 Model finalization
    • 14.2.1 Regression on Time
    • 14.2.2 Regression on Time With Seasonal Components
    • 14.2.3 Naive Approach
    • 14.2.4 Simple Average
    • 14.2.5 Moving Average(MA)
    • 14.2.6 Simple Exponential Smoothing
    • 14.2.7 Holt's Linear Trend Method (Double Exponential Smoothing)
    • 14.2.8 Holt-Winters Method - Additive seasonality
    • 14.2.9 Holt-Winters Method - Multiplicative Model
• 15.AUTO REGRESSIVE Models
  • 15.1 Random Walk
  • 15.2 ARIMA Model
  • 15.3 Auto ARIMA
• 16.References