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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 third 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? |
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| 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 |
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| 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 Log Returns |
TS Basics |
ADF Test - 1 ADF Test - 2 |
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| 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 |
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| WEEK 2 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 1 |
This week begins with an exploration of different data preprocessing and imputation techniques for handling missing data in time series. |
Data Preprocessing |
Imputation Methods (Blog) Imputation Methods (Video) |
Imputation Notebook |
| Day 2 | Now we will learn time series modelling method ARMA & ARIMA. Here we combine autoregression and moving average method |
ARMA ARIMA |
Notebook | Video on the Notebook |
| Day 3 | Now, we use our knowledge of time series to model financial data. |
Forecasting Future Sales Using ARIMA and SARIMAX | Stock Market Price Trend Prediction Using Time Series Forecasting | |
| Day 4 | Now that we’ve covered the basics of time series, it’s time to explore Facebook Prophet — an open-source algorithm designed for building powerful and accurate time series models. |
Basics of Prophet 1 Basics of Prophet 2 |
Sample Forecasting using Prophet | Diving deeper into Prophet Notebook |
| Day 5 | Let's learn how to forecast complex time series data using multivariate and multiple time series models, and understand event-time predictions through survival analysis. | Multivariate TS Forecasting | Multiple Time series Modeling; | Follow-along Video |
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| WEEK 3 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 | |
|---|---|---|---|---|---|
| Day 1 |
We will be using time series techniques for sales forecasting. | .Kaggle notebook | Sales & Demand Forecasting | ||
| Day 2 | Ever wondered how to model a time series in which variance is varying with time? In such a scenario ARCH/GARCH model is what one needs. Also, today we learn about survival analysis | Theoretical Aspects of ARCH/GARCH Volatility Forecasting (GARCH & ARCH) |
Introduction to Survival Analysis | Notebook | |
| Day 3 | Don't want to code a lot? Let's learn about AutoTS(Automatic Time Series), which is a software tool that automates machine learning (AutoML) to build and deploy time series forecasting models. | Reader [only up to Ensemble, Caveats and Advice are optional] |
AutoML TS |
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| Day 4 | Today, we will be learning about the Darts library which is available in python and has some great tools for making time series forecasting easier. | Darts Tutorial |
Notebook |
Anomaly Detection | |
| Day 5 | Let's have a look at how neural networks and deep learning approach can help in time series analysis. | MLP [Optional] RNN and LSTM |
Gradient Descent |
Forward & Backward Propagation Code from Scratch |
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| WEEK 4 | WHAT'S IN THERE | TASK 1 | TASK 2 | TASK 3 |
|---|---|---|---|---|
| Day 1 |
This notebook will take you through models such as LSTM and RNN to accomplish these tasks. | Recurrent Neural Networks |
Understanding LSTM |
Notebook Watch the video corresponding to the notebook (if required) |
| Day 2 | From where we left off, now let's continue to expand our knowledge of using deep learning model to multivariate time series forecasting | Theoretical aspects of Multivariate Time Series forecasting using DL [Refresher] | End-to-End Multivariate TS Modelling using LSTM |
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| Day 3 | Sometimes time series data have a natural hierarchical structure and we use Hierarchical time series analysis to analyse and model such data | Forecasting Hierarchial TS |
Coding Hierarchical Time Series forecasting Part 1 |
Coding Hierarchical Time Series forecasting Part 2 |
| Day 4 | Let's explore how hybrid models are made for time series forecasting with their applications in real-world |
Forecasting Competition |
N Beats |
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| Day 5 | Let's see how our time-series models are performing on unseen data using different validation methods. Also, now we know how to use ML in time series forecasting. Let's see how not to use ML in time series forecasting (Task 3) |
Validation Methods - Video |
Validation Methods - notebook |
Article |
| LINK | DEADLINE | INSTRUCTIONS |
|---|---|---|
| Assessment 1 |
11:59 PM on May 2, 2025 | This is a strict deadline and it won't be extended |
| Submission Link |
The same as above | N/A |
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