Applied Time Series Analysis and Forecasting in Python
Comprehend the need to normalize data when comparing different time series.
Forecasting Model,Financial Decision-Making,Artificial Intelligence,Development,Data Science
Lectures -13
Resources -1
Duration -8 hours
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Course Description
How does a commercial bank forecast the expected performance of their loan portfolio?
Or how does an investment manager estimate a stock portfolio’s risk?
Which are the quantitative methods used to predict real-estate properties?
If there is some time dependency, then you know it - the answer is: time series analysis.
This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.
In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:
- Easy to understand
- Comprehensive
- Practical
- To the point
- Packed with plenty of exercises and resources
But we know that may not be enough.
We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…
Welcome to Time Series Analysis in Python!
The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.
We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.
Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.
With these tools we will master the most widely used models out there:
- AR (autoregressive model)
- MA (moving-average model)
- ARMA (autoregressive-moving-average model)
- ARIMA (autoregressive integrated moving average model)
- ARIMAX (autoregressive integrated moving average model with exogenous variables)
- SARIA (seasonal autoregressive moving average model)
- SARIMA (seasonal autoregressive integrated moving average model)
- SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
- ARCH (autoregressive conditional heteroscedasticity model)
- GARCH (generalized autoregressive conditional heteroscedasticity model)
- VARMA (vector autoregressive moving average model)
We know that time series is one of those topics that always leaves some doubts.
Until now.
This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.
This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:
stationarity and augmented Dicker-Fuller test
seasonality
white noise
random walk
autoregression
moving average
ACF and PACF,
Model selection with AIC (Akaike's Information Criterion)
Then, we move on and apply more complex statistical models for time series forecasting:
ARIMA (Autoregressive Integrated Moving Average model)
SARIMA (Seasonal Autoregressive Integrated Moving Average model)
SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)
We also cover multiple time series forecasting with:
VAR (Vector Autoregression)
VARMA (Vector Autoregressive Moving Average model)
VARMAX (Vector Autoregressive Moving Average model with exogenous variable)
Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:
Simple linear model (1 layer neural network)
DNN (Deep Neural Network)
CNN (Convolutional Neural Network)
LSTM (Long Short-Term Memory)
CNN + LSTM models
ResNet (Residual Networks)
Autoregressive LSTM
Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.
Goals
Comprehend the need to normalize data when comparing different time series.
Encounter special types of time series like White Noise and Random Walks.
Learn about accounting for "unexpected shocks" via moving averages.
Start coding in Python and learn how to use it for statistical analysis.
Prerequisites
Beginner data scientists looking to gain experience with time series
People interested in quantitative finance.
Aspiring data scientists.
Programmers who want to specialize in finance.
![Applied Time Series Analysis and Forecasting in Python Applied Time Series Analysis and Forecasting in Python](https://d3mxt5v3yxgcsr.cloudfront.net/courses/15020/course_15020_image.jpg)
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Applied Time Series Analysis and Forecasting in Python
1 Lectures
-
Introduction to Applied Time Series Analysis and Forecasting in Python 02:06 02:06
PYTHON - Introduction to Basics of Python for Beginners
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Python - Implementation Of Lambda, Recursion, Functions.
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Python - Understand Of Libraries,Exploratory Data Analysis,Descriptive Analysis
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Foundations of Business Statistics for Data Analysis
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
TIME SERIES ANALYSIS - Introduction to Basics of Time Series for Beginners
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Concluding Insights: Mastering Applied Time Series Analysis and Forecasting with Python
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Sample
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![AKHIL VYDYULA](https://www.tutorialspoint.com/assets/profiles/562262/profile/200_3498386-1698323497.jpg)
AKHIL VYDYULA
Hello, I'm Akhil, an Associate Consultant at Atos India with a focus on the Advisory Consulting practice, specializing in Data and Analytics. My professional journey has led me through various facets of data analysis and modeling, particularly in the BFSI sector, where I've had the privilege of overseeing the full lifecycle of development and execution.
My skill set encompasses a wide range of data-related tasks, including data wrangling, feature engineering, algorithm development, model training, and implementation. I thrive on leveraging data mining techniques such as statistical analysis, hypothesis testing, regression analysis, as well as both unsupervised and supervised machine learning processes to extract meaningful insights and drive data-informed decisions. I'm particularly passionate about risk identification through decision models, and I've honed my expertise in Machine Learning Algorithms, Data/Text Mining techniques, and Data Visualization to effectively address these challenges.
Currently, I'm immersed in an exciting Amazon cloud project that involves end-to-end development of ETL processing. In this role, I craft ETL processing code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, execute scripts using EMR services, and load consolidated data into Postgres SQL (RDS/Redshift) on a full, incremental, and live basis. To streamline this process, I've automated it by creating jobs in Step functions, which trigger EMR instances to run scripts in a specific order and send notifications upon execution status changes. The scheduling of these Step functions is achieved through event bridge rules.
Additionally, I've worked extensively with AWS Glue, using it to replicate source data from on-premises systems to raw-layer S3 buckets via AWS DMS services. One of my key strengths lies in my ability to understand the nuances of data and apply the right transformations to convert data from multiple tables into key-value pairs. Furthermore, I've optimized the performance of stored procedures in Postgres SQL to execute second-level transformations by efficiently joining multiple tables and loading the data into final tables.
I'm passionate about harnessing the power of data to drive actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, feel free to connect with me. Let's explore the endless possibilities that data analytics has to offer!
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