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| WEEK 1 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 | |
|---|---|---|---|---|---|
| Day 1 |
Hey there, excited to start learning? Welcome to the fourth edition of our course on Time Series Analysis and Forecasting We'll start by exploring the fundamental concepts of time series forecasting, along with key statistical principles, to build a strong foundation for what’s ahead. |
What is TSA? Why is TSA important? |
Theoretical aspects of Time Series forecasting |
What are p-values? |
|
| Day 2 | Today, we'll delve into hypothesis testing, understand autocorrelation, and explore essential Python tools for time series analysis. |
Hypothesis Testing - 1 Hypothesis Testing - 2 (till 4:37) Hypothesis Testing - 3 |
Understanding Autocorrelation and Partial Autocorrelation Functions | Python basics for TSA (till module 6) | |
| Day 3 | Now, let's explore the fundamentals of time series, key modeling techniques, and how to analyze residuals and prediction intervals. |
Handbook for Handling Date Time Log Returns |
Residuals and Prediction Intervals | Residuals Theory |
|
| Day 4 | Today, we'll dive into key time series concepts like white noise, log returns, and stationarity. We'll also explore the Augmented Dickey-Fuller (ADF) test to assess stationarity in time series data. |
White Noise |
TS Basics |
ADF Test - 1 ADF Test - 2 |
|
| Day 5 | Before applying time series models we need to know data analysis and smoothing methods to get rid of noise. |
Analysis of TS Data | Smoothing Methods (upto 40 minutes) | Notebook on Smoothing Methods |
Coming soon!
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Coming soon!
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Coming soon!
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Coming soon!
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