🥤 MAKAHO (for MAnn-Kendall Analysis of Hydrological Observations) is an interactive cartographic visualization system that allows to calculate trends present in data from hydrometric stations with flows which are little influenced by human actions

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.

This repository analyzes the Multivariate workload data of Google Cluster machines.

🌾 R package to provide an efficient and simple solution to aggregate and analyze the stationarity of time series

🛠️ R toolbox to provide a simple way of interacting with all the code necessary to carry out hydrological stationnarity analysis for the Agence de l'Eau Adour-Garonne (AEAG)

Unit root tests in Rust

Forecasting Wine Sales of Two Different types of Wine. After thorough Data Analysis, different models have been used and tested such as Exponential Smoothing Models, Regression, Naive Forecast, Simple Average, Moving Average. Stationarity of the data is checked. Automated Version of ARIMA/SARIMA Model built. Comparison of Models.

This repository covers essential techniques for time series analysis and forecasting. It covers data manipulation and visualization using Numpy and Pandas, time series analysis with Statsmodels, ARIMA models, deep learning methods like RNNs, LSTM, GRU, etc. and Facebook's Prophet library.

This project aims to delve into the intricate relationships between Foreign Direct Investment (FDI), government debt, and Gross Domestic Product (GDP) using a detailed empirical analysis.

Unemployment Rate Forecasting using Time Series techniques, leveraging Statsmodels, LSTMs, and Facebook's Prophet library to predict future unemployment trends. The project includes model comparison, hyperparameter tuning, and visualization of forecasted results.

Time Series forecasting using Seasonal ARIMA. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots. Transformed series to make it stationary

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

Statistical toolkit to make time-series stationary

Code for analyzing statistical features of several precipitation stations

Automating time series stationarity tests