Applied Time Series Analysis and Forecasting in Python
Applied Time Series Analysis and Forecasting in Python, available at $64.99, has an average rating of 4.5, with 12 lectures, based on 3 reviews, and has 1431 subscribers.
You will learn about 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. Comprehend the need to normalize data when comparing different time series. This course is ideal for individuals who are Aspiring data scientists. or Professional data scientists who need to analyze time series or Deep learning beginners curious about times series It is particularly useful for Aspiring data scientists. or Professional data scientists who need to analyze time series or Deep learning beginners curious about times series.
Enroll now: Applied Time Series Analysis and Forecasting in Python
Summary
Title: Applied Time Series Analysis and Forecasting in Python
Price: $64.99
Average Rating: 4.5
Number of Lectures: 12
Number of Published Lectures: 12
Number of Curriculum Items: 12
Number of Published Curriculum Objects: 12
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- 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.
- Comprehend the need to normalize data when comparing different time series.
Who Should Attend
- Aspiring data scientists.
- Professional data scientists who need to analyze time series
- Deep learning beginners curious about times series
Target Audiences
- Aspiring data scientists.
- Professional data scientists who need to analyze time series
- Deep learning beginners curious about times series
How does a commercial bank forecast the expected performance of its 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 timelessbut 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 modelling that comes afterwards.
Then throughout the course, we will work with several Python libraries, providing you with complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, finance, ARCH and prima.
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 – notebook 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:
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stationarity and augmented Dicker-Fuller test
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seasonality
-
white noise
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random walk
-
autoregression
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moving average
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ACF and PACF,
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Model selection with AIC (Akaike’s Information Criterion)
Then, we move on and apply more complex statistical models for time series forecasting:
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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:
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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:
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Simple linear model (1-layer neural network)
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DNN (Deep Neural Network)
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CNN (Convolutional Neural Network)
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LSTM (Long Short-Term Memory)
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CNN + LSTM models
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ResNet (Residual Networks)
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Autoregressive LSTM
Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.
Course Curriculum
Chapter 1: PYTHON – Introduction to Basics of Python for Beginners
Lecture 1: Python – Data Structures (Lists, Tuple, Dictionary) and String Manipulations
Lecture 2: Python – Implementation Of Lambda, Recursion, Functions.
Lecture 3: Python – Understand Of Libraries,Exploratory Data Analysis,Descriptive Analysis
Chapter 2: Foundations of Business Statistics for Data Analysis
Lecture 1: Introduction to statistics and Measures of central tendencies
Lecture 2: Central Limit Theorem – CLT
Lecture 3: Distributions and Correlations
Lecture 4: PDF & CDF and Hypothesis Testing
Chapter 3: TIME SERIES ANALYSIS – Introduction to Basics of Time Series for Beginners
Lecture 1: TIME SERIES – Characteristics and Decomposition of Time Series Data
Lecture 2: TIME SERIES – Best Practices of Probability, Statistics and Forecasting Models
Lecture 3: TIME SERIES – Practical Understanding of Time Series Analysis with Medical Data
Chapter 4: Capstone Project : UK_Road_Accident_Timeseries_Forecasting_EDA
Lecture 1: UK_Road_Accident_Timeseries_Forecasting_EDA
Lecture 2: Forecast UK Accident rates based on Number of Casualties on SARIMA,FbP,LSTM's
Instructors
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Akhil Vydyula
Data Scientist | Data & Analytics Specialist | Entrepreneur
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 0 votes
- 5 stars: 2 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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