README.md

TSSDA-Lab

This repository contains the programs that I worked out in Time Series and Sequential Data Analysis Lab.

Index

• Lab 1: Introduction
  • Prelab: Exploring Pandas and Matplotlib
  • Task 1: Viewing Time Series Data
• Lab 2: Basic Feature Engineering with Time Series Data
  • Task 2: Feature Engineering Program
• Lab 3: ITSM Tool Exploration
• Lab 4: Data Visualization and Sampling
  • Task 3: Data Visualization Program
  • Task 4: Data Sampling Program
• Lab 5: Check the Stationality, Trend and Seasonality
  • Task 5A
  • Task 5B
• Lab 6: Check the Stationality, Trend and Seasonality
  • Task 5C
  • Task 6A: Check for the ACF and PACF of the data

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• Lab 7: ACF, PACF, AR, MA, ARMA calculations

  • Task 6A: Check for the ACF and PACF of the data
  • Task 6B: Check for the ACF and PACF of the data

• Lab 7 (continued): Implement the differencing method and polynomial model to create a seasonally adjusted time series

  • Task 7
  • Task 7A
  • Task 7A (using Sunspots dataset)

• Lab 8: ARMA; OLS, GLS, Rolling Regression; Evaluating Performance Metrics

  • Task 8: ARMA
  • Task 8A: OLS, GLS, Rolling Regression
  • Task 8B: Evaluating Performance Metrics (only for reference)

• Lab 9: ARIMA; Seasonal ARIMA

  • Task 9: ARIMA
  • Task 10: Seasonal ARIMA
  • Task 11: Multivariate ARIMA forecasting technique

• Lab 10: Implement the state space modelling and visualize the same.

  • Task 12A: Implement Data GARCH model (without OLS)
  • Task 12A: Implement Data GARCH model (with OLS)

• Lab 11: Implement the state space modelling and visualize the same.

  • Task 12A: Implement Data GARCH model (without OLS)
  • Task 12B: Kalman Filter on Air Quality Dataset
  • Task 12B: Kalman Filter on Yahoo Finance Dataset (miscellaneous)
  • Task 12C: Explore and visualize data connected with extreme weather events (not required for record)

• Complete TSSDA Codes Program

Prerequisites

Python and packages in the requirements.txt file installed.

Note

You can install all the required packages using the command pip install -r requirements.txt.

Working with Conda

If you are using conda to manage your environments, you can create a new environment for this repository with the command conda create -n tssda and activate it with the command conda activate tssda.

Tip

For faster environment solving in Conda, I would suggesting using the libmamba solver. You can set it as the default solver using the command conda config --set solver libmamba.

Then, you can install all the required packages using the command conda install --file requirements.txt.

Container Image

Alternatively, you can use the container image I created with all the packages preinstalled.

You can install it in Distrobox with the command distrobox create -i ghcr.io/kbdharun/tssda-lab-image:latest -n tssda and use it with the command distrobox enter tssda.

Additionally, you can verify the authenticity of the container image using cosign (download the cosign.pub file from here and execute the following command):

cosign verify --key cosign.pub ghcr.io/kbdharun/tssda-lab-image:latest