Bayesian Change-Point Detection and Time Series Decomposition

Analyzing seasonality with Fourier transforms

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.

Time Serial Methods and Forecasting (RegARIMA and ARMAX)

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

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

Pyriodicity provides an intuitive and easy-to-use Python implementation for periodicity detection in univariate signals.

😲🤑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 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.

Implemented time series forecasting to predict future orders for Glovo, utilizing models such as ARIMA/SARIMA, LASSO, XGBoost, and Linear Regression.

Time Series Forecasting

Data Science - Forecasting

To explore diatom community structure dynamic and distribution over time (8years) around Australian coastal water (IMOS NRS station) and to identify abiotic (e.i. temperature, nutrients.. etc) and biotic (bacteria) factor that influence them. This was through analysis of 18S and 16S rRNA data available form the Australaian Microbiome Initiative.

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metric.

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

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.

Financial time series forecasting using R

Time series modeling to predict fares for Sweet Lift Taxi Company. Predictions will be used to allocate drivers for peak hours.

Forecasting the car sales time series dataset which is populated with a factor of seasonality in the dataset and the action of forecasting is performed using prophet library in python.

The object of this project is to analyze website data with over 30,000 users and more than 1,188,124 pageviews to evaluate website performance and to understand product level performance. I use conversion funnel analysis to understand customer behavior in order to improve website traffic via different channels.