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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 secondever edition of our course on Time Series Analysis and Forecasting First, we will learn the theoretical aspects of time series forecasting and some basic statistics which will be required afterwards. 
What are pvalues? Hypothesis Testing 1 Hypothesis Testing 2 
Theoretical aspects of Time Series forecasting 
Autocorrelation and partial correlation? Autocorrelation  mathematical aspects 
Day 2  Today we will learn about prediction intervals and some python tools needed for handling date and time indexed data. 
Handbook for handling date time 
Prediction Interval  Prediction Interval blog 
Day 3  Now let us learn the basics of time series and different models used in time series analysis. 
White Noise Log Returns 
TSBasics  ADF Test1 ADF Test2 ADF Test3 
Day 4  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 mins) Notebook 

Day 5  Now ,let's learn how to handle missing values in the data . 
Basics of Imputation  Imputation following Data Analysis 
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WEEK 2  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1 
After learning the basics of time series , now we will learn about Facebook Prophet which is an opensource algorithm , used to generate effective time series models. 
Basics of Prophet 1 Basics of Prophet 2 
Sample Forecasting using Prophet 
Multivariate TS forecasting 
Day 2  Let's see how to fit a curve with the help of Prophet. The attached notebook will take you through various aspects of Prophet. 
Diving deeper into Prophet Notebook 
Multiple Time series modelling  
Day 3  Now we will learn time series modelling method ARMA & ARIMA. Here we combine autoregression and moving average method. 
ARMA  ARIMA  
Day 4  Let's see how to write the code for above models. The attached notebook and the followalong video corresponding to the same notebook will help in this. 
Notebook on ARMA/ARIMA  Followalong Video on ARMA/ARIMA  
Day 5  Let's now use our knowledge of time series for modelling financial data.  Notebook (refer to video in task 2 for more insights)  Followalong 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  Corresponding Video on ARMA/ ARIMA 

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.  Theoretical Aspects of ARCH/GARCH 
Modelling ARCH/GARCH models  
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] 
Auto 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 
Darts tutorial notebook, along with additional information on AutoTS 
Video corresponding to the notebook, (watch from 30 mins to 55 mins)  
Day 5  Let's have a look at how neural networks and deep learning approach can help in time series analysis. This notebook will take you through models such as LSTM and RNN to accomplish these tasks  [Optional] RNN and LSTM [Optional] RNN and LSTM 
Kaggle Notebook explaining RNNs in TSA 
Watch the video corresponding to the notebook if required 
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WEEK 4  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1 
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 Time Series forecasting using DL 

Day 2  Sometimes time series data have a natural hierarchical structure and we use Hierarchical time series analysis to analyse and model such data  Theoretical aspects  Coding Hierarchical Time Series forecasting Part 1 
Coding Hierarchical Time Series forecasting Part 2 
Day 3  Let's explore how hybrid models are made for time series forecasting with their applications in realworld  ESRNN 
NBEATS 

Day 4  Let's see how our timeseries 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 
Day 5  While it is important to know how to build models, it is also worth noting how we can use the pretrained models for our problems through transfer learning 
Transfer Learning in Time Series 
Optional Video 
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WEEK 5  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1  A hands on example for CNN implementation would be helpful for building essence of what actually goes into hybrid models 
CNN model  CNN model code 

Day 2  The two most famous networks in deep learning are CNN and RNN, and Temporal Convolution Network will help combine the two for our task.  Temporal Convolution Network 
DeepAR 

Day 3  You must have used ChatGPT or something similar which uses a transformer architecture. Today, we will be looking at a model that uses transformers in time series forecasting.  Temporal Fusion Transformer Theory  Temporal Fusion Transformer Code 
LINK  DEADLINE  INSTRUCTIONS 

Assignment 
May 17, 2024  N/A 
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No.  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3  TASK 4  

Topic 1  Neural Networks 
Basic  Gradient Descent 
Forward and Backward Propagation 
Code from Scratch 

Topic 2  Convolutional Neural Network  Theory 
Code from Scratch 

Topic 3  Sequential Modeling  Recurrent Neural Network(RNN) & Long ShortTerm Memory(LSTM)  Transformer  Long ShortTerm Memory(LSTM) Code from Scratch 
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