DesireCourse Net Udemy - Time Series Analysis in Python 2020

1. 01 Introduction/001 What does the course cover.mp447.34MB
2. 02 Setting Up the Environment/002 Setting up the environment - Do not skip please.mp45.97MB
3. 02 Setting Up the Environment/003 Why Python and Jupyter.mp425.19MB
4. 02 Setting Up the Environment/004 Installing Anaconda.mp426.63MB
5. 02 Setting Up the Environment/005 Jupyter Dashboard - Part 1.mp49.76MB
6. 02 Setting Up the Environment/006 Jupyter Dashboard - Part 2.mp420.03MB
7. 02 Setting Up the Environment/007 Installing the Necessary Packages.mp47.83MB
8. 03 Introduction to Time Series in Python/010 Introduction to Time-Series Data.mp447.18MB
9. 03 Introduction to Time Series in Python/011 Notation for Time Series Data.mp412.17MB
10. 03 Introduction to Time Series in Python/012 Peculiarities of Time Series Data.mp426.8MB
11. 03 Introduction to Time Series in Python/013 Loading the Data.mp410.16MB
12. 03 Introduction to Time Series in Python/014 Examining the Data.mp439.83MB
13. 03 Introduction to Time Series in Python/015 Plotting the Data.mp421.23MB
14. 03 Introduction to Time Series in Python/016 The QQ Plot.mp416.29MB
15. 04 Creating a Time Series Object in Python/017 Transforming String inputs into DateTime Values.mp427.91MB
16. 04 Creating a Time Series Object in Python/018 Using Date as an Index.mp416.56MB
17. 04 Creating a Time Series Object in Python/019 Setting the Frequency.mp413.45MB
18. 04 Creating a Time Series Object in Python/020 Filling Missing Values.mp429.96MB
19. 04 Creating a Time Series Object in Python/021 Adding and Removing Columns in a Data Frame.mp416.26MB
20. 04 Creating a Time Series Object in Python/022 Splitting Up the Data.mp420.98MB
21. 05 Working with Time Series in Python/024 White Noise.mp446.36MB
22. 05 Working with Time Series in Python/025 Random Walk.mp432.41MB
23. 05 Working with Time Series in Python/026 Stationarity.mp421.56MB
24. 05 Working with Time Series in Python/027 Determining Weak Form Stationarity.mp433.84MB
25. 05 Working with Time Series in Python/028 Seasonality.mp434.23MB
26. 05 Working with Time Series in Python/029 Correlation Between Past and Present Values.mp414.08MB
27. 05 Working with Time Series in Python/030 The Autocorrelation Function (ACF).mp430.65MB
28. 05 Working with Time Series in Python/031 The Partial Autocorrelation Function (PACF).mp427.18MB
29. 06 Picking the Correct Model/032 Picking the Correct Model.mp422.96MB
30. 07 Modeling Autoregression The AR Model/033 The Autoregressive (AR) Model.mp445.3MB
31. 07 Modeling Autoregression The AR Model/034 Examining the ACF and PACF of Prices.mp433.08MB
32. 07 Modeling Autoregression The AR Model/035 Fitting an AR(1) Model for Index Prices.mp431.63MB
33. 07 Modeling Autoregression The AR Model/036 Fitting Higher-Lag AR Models for Prices.mp463.16MB
34. 07 Modeling Autoregression The AR Model/037 Using Returns Instead of Prices.mp431.38MB
35. 07 Modeling Autoregression The AR Model/038 Examining the ACF and PACF of Returns.mp415.67MB
36. 07 Modeling Autoregression The AR Model/039 Fitting an AR(1) Model for Index Returns.mp413.37MB
37. 07 Modeling Autoregression The AR Model/040 Fitting Higher-Lag AR Models for Returns.mp426.88MB
38. 07 Modeling Autoregression The AR Model/041 Normalizing Values.mp433.08MB
39. 07 Modeling Autoregression The AR Model/042 Model Selection for Normalized Returns (AR).mp419.83MB
40. 07 Modeling Autoregression The AR Model/043 Examining the AR Model Residuals.mp428.78MB
41. 07 Modeling Autoregression The AR Model/044 Unexpected Shocks from Past Periods.mp416.77MB
42. 08 Adjusting to Shocks The MA Model/045 The Moving Average (MA) Model.mp429.46MB
43. 08 Adjusting to Shocks The MA Model/046 Fitting an MA(1) Model for Returns.mp421.52MB
44. 08 Adjusting to Shocks The MA Model/047 Fitting Higher-Lag MA Models for Returns.mp455.85MB
45. 08 Adjusting to Shocks The MA Model/048 Examining the MA Model Residuals for Returns.mp433.48MB
46. 08 Adjusting to Shocks The MA Model/049 Model Selection for Normalized Returns (MA).mp419.1MB
47. 08 Adjusting to Shocks The MA Model/050 Fitting an MA(1) Model for Prices.mp428.34MB
48. 08 Adjusting to Shocks The MA Model/051 Past Values and Past Errors.mp420.46MB
49. 09 Past Values and Past Errors The ARMA Model/052 The Autoregressive Moving Average (ARMA) Model.mp428.34MB
50. 09 Past Values and Past Errors The ARMA Model/053 Fitting a Simple ARMA Model for Returns.mp428.41MB
51. 09 Past Values and Past Errors The ARMA Model/054 Fitting a Higher-Lag ARMA Model for Returns - Part 1.mp439.54MB
52. 09 Past Values and Past Errors The ARMA Model/055 Fitting a Higher-Lag ARMA Model for Returns - Part 2.mp438.18MB
53. 09 Past Values and Past Errors The ARMA Model/056 Fitting a Higher-Lag ARMA Model for Returns - Part 3.mp443.81MB
54. 09 Past Values and Past Errors The ARMA Model/057 Examining the ARMA Model Residuals of Returns.mp451.27MB
55. 09 Past Values and Past Errors The ARMA Model/058 ARMA for Prices.mp455.97MB
56. 09 Past Values and Past Errors The ARMA Model/059 ARMA Models and Non-Stationary Data.mp414.87MB
57. 10 Modeling Non-Stationary Data The ARIMA Model/060 The Autoregressive Integrated Moving Average (ARIMA) Model.mp446.9MB
58. 10 Modeling Non-Stationary Data The ARIMA Model/061 Fitting a Simple ARIMA Model for Prices.mp439.21MB
59. 10 Modeling Non-Stationary Data The ARIMA Model/062 Fitting a Higher-Lag ARIMA Model for Prices - Part 1.mp441.85MB
60. 10 Modeling Non-Stationary Data The ARIMA Model/063 Fitting a Higher-Lag ARIMA Model for Prices - Part 2.mp443.65MB
61. 10 Modeling Non-Stationary Data The ARIMA Model/064 Higher Levels of Integration.mp424.41MB
62. 10 Modeling Non-Stationary Data The ARIMA Model/065 Using ARIMA Models for Returns.mp424.4MB
63. 10 Modeling Non-Stationary Data The ARIMA Model/066 Outside Factors and the ARIMAX Model.mp424.21MB
64. 10 Modeling Non-Stationary Data The ARIMA Model/067 Seasonal Models - SARIMAX.mp446.95MB
65. 10 Modeling Non-Stationary Data The ARIMA Model/068 Predicting Stability.mp416.98MB
66. 11 Measuring Volatility The ARCH Model/069 The Autoregressive Conditional Heteroscedasticity (ARCH) Model.mp443.03MB
67. 11 Measuring Volatility The ARCH Model/070 Volatility.mp428.15MB
68. 11 Measuring Volatility The ARCH Model/071 A More Detailed Look of the ARCH Model.mp443.36MB
69. 11 Measuring Volatility The ARCH Model/072 The arch_model Method.mp455.86MB
70. 11 Measuring Volatility The ARCH Model/073 The Simple ARCH Model.mp452.89MB
71. 11 Measuring Volatility The ARCH Model/074 Higher-Lag ARCH Models.mp428.47MB
72. 11 Measuring Volatility The ARCH Model/075 An ARMA Equivalent of the ARCH Model.mp412.4MB
73. 12 An ARMA Equivalent of the ARCH The GARCH Model/076 The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model.mp424.41MB
74. 12 An ARMA Equivalent of the ARCH The GARCH Model/077 The ARMA and the GARCH.mp418.09MB
75. 12 An ARMA Equivalent of the ARCH The GARCH Model/078 The Simple GARCH Model.mp425.46MB
76. 12 An ARMA Equivalent of the ARCH The GARCH Model/079 Higher-Lag GARCH Models.mp429.76MB
77. 12 An ARMA Equivalent of the ARCH The GARCH Model/080 An Alternative to the Model Selection Process.mp413.37MB
78. 13 Auto ARIMA/081 Auto ARIMA.mp443.06MB
79. 13 Auto ARIMA/082 Preparing Python for Model Selection.mp411.44MB
80. 13 Auto ARIMA/083 The Default Best Fit.mp441.1MB
81. 13 Auto ARIMA/084 Basic Auto ARIMA Arguments.mp487.42MB
82. 13 Auto ARIMA/085 Advanced Auto ARIMA Arguments.mp440.83MB
83. 13 Auto ARIMA/086 The Goal Behind Modelling.mp410.63MB
84. 14 Forecasting/087 Introduction to Forecasting.mp451.25MB
85. 14 Forecasting/088 Simple Forecasting Returns with AR and MA.mp428.8MB
86. 14 Forecasting/089 Intermediate (MAX Model) Forecasting.mp439.98MB
87. 14 Forecasting/090 Advanced (Seasonal) Forecasting.mp424.93MB
88. 14 Forecasting/091 Auto ARIMA Forecasting.mp428.39MB
89. 14 Forecasting/092 Pitfalls of Forecasting.mp447.88MB
90. 14 Forecasting/093 Forecasting Volatility.mp436.58MB
91. 14 Forecasting/094 Forecasting Appendix Multivariate Forecasting.mp457.67MB
92. 15 Business Case/095 Business Case - A Look Into the Automobile Industry.mp4186.23MB