Financial Analysis with ARIMA and Time Series Forecasting

[0dayddl]

994.29 MB | 00:20:20 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

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)

]
Screenshot

Spoiler

Fikper

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

FileAxa

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

TurboBit

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

 

[0dayddl]

994.29 MB | 00:20:20 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

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)

]
Screenshot

Spoiler

Fikper

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

FileAxa

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

TurboBit

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

 

[0dayddl]

Financial Analysis with ARIMA and Time Series Forecasting
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | 6.5 Hours Duration | 1.29 GB
Genre: eLearning | Language: English​

Begin with an introduction to time series analysis, providing a solid foundation for understanding the nature and structure of time series data. You'll explore key concepts such as modeling versus predicting, and learn essential data transformation techniques including power, log, and Box-Cox transformations. These fundamentals set the stage for more advanced topics.
As you delve deeper, you'll encounter a thorough examination of financial time series. You'll learn about random walks, the random walk hypothesis, and the importance of baseline forecasts. The course then transitions to a comprehensive study of ARIMA models. You'll explore autoregressive models (AR), moving average models (MA), and the combination of these in ARIMA. Practical coding sessions will reinforce your understanding, allowing you to apply stationarity tests, ACF, PACF, and Auto ARIMA techniques to real financial data.
The latter part of the course focuses on the application of ARIMA models in forecasting. You'll learn how to implement ARIMA in various scenarios, from stock returns to sales data. The course wraps up with a detailed guide on forecasting out-of-sample data, ensuring you can apply your new skills in real-world situations. Supplementary sections offer guidance on setting up your coding environment and additional help for Python beginners.
What you will learn
Understand and analyze time series data
Implement data transformations for improved modeling
Apply ARIMA models to financial data
Perform stationarity tests and utilize ACF/PACF
Forecast financial data using ARIMA techniques
Develop data-driven decision-making skills
Audience
This course is designed for financial professionals, data analysts, and enthusiasts with a basic understanding of statistics and Python. Prior experience with financial data is beneficial but not required.
About the Author
Lazy Programmer:
The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.

DDownload

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

NitroFlare

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!