Bayesian Change-Point Detection and Time Series Decomposition

Analyzing seasonality with Fourier transforms

Pyriodicity provides an intuitive and efficient Python implementation of periodicity length detection methods in univariate signals.

Forecast the Airlines Passengers. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

A comprehensive resource for mastering data analysis in Excel with Microsoft Copilot. Features the dataset and prompts from the YouTube tutorial, plus an expanded guide with best practices and advanced techniques.

Examined 60 years of Mauna Loa CO2 data, utilizing Python, Jupyter, and essential libraries for preprocessing and advanced modeling, revealing key atmospheric trends.

Time series analysis showing trend, seasonality, and periodicity decomposition; and forecasting using Facebook Prophet. The analysis makes extensive use of indexing data tools and of the Pandas and Datetime libraries.

😲🤑Method for Investors and Traders to make Buying and Selling Decisions. 😄Fundamental Market Analysis is about using Real data to evaluate a Stock's Value📊 📈 📉

Time Serial Methods and Forecasting (RegARIMA and ARMAX)

Feature insights into Somalia's severe acute malnutrition admission data

Feature insights into Afghanistan's acute malnutrition admission data

Generating "Market Seasonality" Chart for Any Market listed on Yahoo Finance

eseas is a Python package that acts as a wrapper for the jwsacruncher Java package. This tool allows users to process Demetra workspace XML files, create batch files, execute them, and collect the desired outputs into individual Excel files.

Customer Profile & Shopping Behavior Analysis is an R-based project analyzing customer data from retail stores, focusing on segmentation, seasonal trends, and market behaviors.

FFT-accelerated Singular Spectrum Analysis in C. SSA at the speed of FFT. Decompose time series into trend, seasonality & noise. O(N log N) Hankel matvec.

Used First Difference Method for Stationarity of the Time Series and then Used ARIMA & SARIMA to predict the values and based on the prediction, checked if the series contains Seasonal Patterns in it or not

This is my contribution to the 2024 ML Marathon at UW-Madison where I worked in a team of 4 to predict CO2 Emissions in Rwanda using time-series forecasting.

Financial time series forecasting using R

Uncover hidden patterns and relationships within Bitcoin trading volume using rigorous time series techniques. Develop a robust forecasting model capable of accurately predicting future volume levels.

The repository presents the notebooks and models used for my experimental bachelor thesis entitled: "Experimental Study of the Steel Market Through CNN-LSTM Deep Learning Models: Practical Applications for Cost Reduction in Industries". Used Python language and Tensorflow/Keras