994.29 MB | 00:20:20 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
001 Introduction and Outline (12.56 MB)
002 Special Offer (3.16 MB)
001 Warmup (Optional) (11.18 MB)
002 Where to get the code (8.88 MB)
001 What is a Time Series (13.42 MB)
002 Modeling vs Predicting (6.28 MB)
003 Power, Log, and Box-Cox Transformations (16.04 MB)
004 Suggestion Box (0310) (11.13 MB)
001 Financial Time Series Primer (23.55 MB)
002 Random Walks and the Random Walk Hypothesis (33.63 MB)
003 The Naive Forecast and the Importance of Baselines (15.47 MB)
001 ARIMA Section Introduction (11.95 MB)
002 Autoregressive Models - AR(p) (28.22 MB)
003 Moving Average Models - MA(q) (5.85 MB)
004 ARIMA (22.47 MB)
005 ARIMA in Code (60.24 MB)
006 Stationarity (29.38 MB)
007 Stationarity in Code (27.85 MB)
008 ACF (Autocorrelation Function) (20.72 MB)
009 PACF (Partial Autocorrelation Function) (13.96 MB)
010 ACF and PACF in Code (pt 1) (18.98 MB)
011 ACF and PACF in Code (pt 2) (15.72 MB)
012 Auto ARIMA and SARIMAX (20.76 MB)
013 Model Selection, AIC and BIC (23.91 MB)
014 Auto ARIMA in Code (47.94 MB)
015 Auto ARIMA in Code (Stocks) (47.61 MB)
016 ACF and PACF for Stock Returns (19.13 MB)
017 Auto ARIMA in Code (Sales Data) (31.44 MB)
018 How to Forecast with ARIMA (20 MB)
019 Forecasting Out-Of-Sample (3.33 MB)
020 ARIMA Section Summary (6.83 MB)
001 Pre-Installation Check (11.03 MB)
002 Anaconda Environment Setup (66.43 MB)
003 How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow (49.08 MB)
001 How to Code Yourself (part 1) (29.57 MB)
002 How to Code Yourself (part 2) (19.07 MB)
003 Proof that using Jupyter Notebook is the same as not using it (34.49 MB)
004 How to use Github & Extra Coding Tips (Optional) (29.18 MB)
001 How to Succeed in this Course (Long Version) (17.28 MB)
002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced (41.56 MB)
003 What order should I take your courses in (part 1) (28.15 MB)
004 What order should I take your courses in (part 2) (36.69 MB)]
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