Introduction to Time Series Analysis

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Section 1: Introduction to Time Series

  1. Introduction - What is a time series and why it matters a lot
  2. Panel Data vs Cross Sectional Data
  3. Download Resources
  4. Time Series Visualizations for Quick Insights

Section 2: Components of Time Series

  1. Components of time series.
  2. Difference between Seasonlity and Cyclic pattern
  3. Additive vs Multiplicative Time Series
  4. Seasonal Index Computation - Theory
  5. Workout Seasonal Index Computation
  6. Course Review

Section 3: Classical vs STL Decomposition

  1. Classical decomposition using Statsmodels
  2. STL Decomposition - video

Section 4: Detecting Anomalies

  1. How to spot anamoly observations in time series

Section 5: Multi Seasonal Time Series (MSTL) Decomposition

  1. Multi Seasonal Time Series Decomposition (MSTL)
  2. MSTL Decomposition Code Demo
  3. X13 Decomposition in Python

Section 6: Detrending Methods

  1. How to Detrend a Time series
  2. Detrending - Code Demo - video

Section 7: Deseasonalizing Time Series

  1. How to Deseasonalize Time Series - Approaches
  2. How to measure the strength of Trend and Seasonality

Section 8: ACF and PACF

  1. Lags and Leads
  2. Autocorrelation - video
  3. Partial Autocorrelation Function (PACF)

Section 9: Stationarity of Time Series

  1. What is Stationarity?
  2. Why even bother making a time series stationary
  3. How to make a time series stationary
  4. Statistical tests for stationarity - ADFTest - video
  5. KPSS Test vs ADF Test
  6. Missing values imputation approaches

Section 10: Dealing with Missing Values

  1. Missing values imputation approaches
  2. Customized Imputation Approaches
  3. How to validate missing value imputation
  4. Missing Values Imputation Code Dem
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