Complete Time Series Analysis for Data Science 📊

📈 Complete Time Series Analysis eBook + Python Notebook (Written by me covering all topics): https://topmate.io/ayushi_mishra/1229949


Master Time Series Analysis for Data Science & Data Analysis in 3 hours. This comprehensive Crash Course covers EVERYTHING you need to know, from Stationarity, Forecasting models to Time series data preprocessing. Whether you're a complete beginner or looking for a refresher, this video is your one-stop shop for conquering Data Science Time Series Analysis!

All topics are implemented using Python

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Timestamps:

00:00 Complete Syllabus and importance of time series analysis
11:06 Ebook and Python Notebook Introduction
11:55 Time Series Data
14:49 Time Series Data Characteristics
18:18 Time Series Analysis
22:31 Time Series Decomposition
30:02 Additive and Multiplicative Decomposition methods
33:48 Classical Decomposition
37:35 STL Decomposition using LOESS
38:49 Difference between STL and classical decomposition
42:15 STL decomposition using Python
43:31 Stationarity in Time series
45:28 Why do we need stationary time series data?
49:03 Weak Stationary and Strict Stationary
57:05 Testing for stationarity
57:39 Augmented Dickey-Fuller (ADF) test
1:00:12 Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test
1:03:29 Kolmogorov–Smirnov test (K–S test or KS test)
1:11:15 Non stationary data to stationary data
1:11:34 Differencing
1:14:00 Transformation
1:15:24 Logarithmic Transformation | Power Transformation | Box Cox Transformation
1:16:41 Detrending and seasonal adjustment
1:28:07 White Noise and Random Walk
1:35:28 Time Series Forecasting Models
1:37:19 Autoregressive (AR)
1:41:20 Moving Average (MA)
1:46:14 Autoregressive Moving Average (ARMA)
1:48:10 Autoregressive Integrated Moving Average (ARIMA)
1:51:01 Seasonal Autoregressive Integrated Moving Average (SARIMA)
1:54:51 Vector AutoRegressive (VAR) | Vector Moving Average (VMA) | Vector AutoRegressive Moving Average (VARMA) | Vector AutoRegressive Integrated Moving Average (VARIMA)
1:55:37 Granger causality test
1:57:31 Time Series Forecasting using Python
2:07:58 Smoothing Methods
2:10:00 Moving Average (Simple, Weighted, Exponential)
2:14:44 Exponential Smoothing
2:20:46 Autocorrelation (ACF) and Partial Autocorrelation Function (PACF)
2:28:15 Identifying models from ACF and PACF
2:34:47 Model evaluation metrics
2:34:54 Mean Absolute Error (MAE)
2:36:41 Mean Squared Error (MSE)
2:38:12 Root Mean Squared Error (RMSE)
2:38:44 Mean Absolute Percentage Error (MAPE)
2:39:41 Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
2:43:19 Time series data preprocessing
2:48:28 Resampling


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