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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?

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

Log Returns
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

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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






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/a>

[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
LINK DEADLINE INSTRUCTIONS
Assessment 1




11:59 PM on May 3, 2025
This is a strict deadline and it won't be extended
Submission Link




May 28, 2023
N/A