Introduction to Time Series Analysis

What you'll be able to do

• What is Time series an why it matters so much in industry
• Additive and multiplicative decomposition methods
• Seasonal Index Computations
• Decomposing multi seasonal time series (MSTS), X13 method
• Measure the strength of seasonality and trend
• Detecting stationarity
• Custom approaches to impute missing data

1. 01SEC 1: Introduction to Time Series4 lessons · 30 min

   1. Introduction - What is a time series and why it matters a lotPreview12:11
   2. Panel Data vs Cross Sectional DataPreview5:33
   3. Download Resources1:00
   4. Time Series Visualizations for Quick InsightsPreview11:31
2. 02SEC 2: Components of Time Series6 lessons · 37 min

   1. Components of Time SeriesPreview2:46
   2. Difference between Seasonlity and Cyclic patternPreview4:33
   3. Additive vs Multiplicative Time SeriesPreview5:00
   4. Seasonal Index Computation - TheoryPreview15:53
   5. Workout Seasonal Index Computation7:33
   6. Course Review1:00
3. 03SEC 3: Classical vs STL Decomposition2 lessons · 12 min

   1. Classical decomposition using Statsmodels2:48
   2. STL Decomposition - video8:49
4. 04SEC 4: Detecting Anomalies1 lesson · 9 min

   1. How to spot anamoly observations in time series8:47
5. 05SEC 5: Multi Seasonal Time Series (MSTL) Decomposition3 lessons · 31 min

   1. Multi Seasonal Time Series Decomposition (MSTL)17:12
   2. MSTL Decomposition Code Demo8:51
   3. X13 Decomposition in Python4:27
6. 06SEC 6: Detrending Methods2 lessons · 11 min

   1. How to Detrend a Time series6:15
   2. Detrending - Code Demo - video4:20
7. 07SEC 7: Deseasonalizing Time Series2 lessons · 16 min

   1. How to Deseasonalize Time Series - Approaches7:53
   2. How to measure the strength of Trend and Seasonality8:21
8. 08SEC 8: ACF and PACF3 lessons · 17 min

   1. Lags and Leads4:33
   2. Autocorrelation - video5:18
   3. Partial Autocorrelation Function (PACF)6:51
9. 09SEC 9: Stationarity of Time Series5 lessons · 29 min

   1. What is Stationarity?5:28
   2. Why even bother making a time series stationary5:18
   3. How to make a time series stationary5:03
   4. Statistical tests for stationarity - ADFTest - video7:00
   5. KPSS Test vs ADF Test6:34
10. 10SEC 10: Dealing with Missing Values4 lessons · 24 min

    1. Missing values imputation approaches9:21
    2. Customized Imputation Approaches3:32
    3. How to validate missing value imputation1:44
    4. Missing Values Imputation Code Demo9:52

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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