Stable AutoCorrelation Integral Estimator

This repository contains Python functions for predicting time series.

velocity.py reads in GROMACS trr trajectory (that inclides velocity information) and calculates center of mass translational velocity and angular velocity. It also computes translational and rotational kinetic energies, temperatures, velocity autocorrelation functions and power spectra

Forecast the Airlines Passengers and CocaCola Prices data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.

The AutoCorrelation Integral Drill (ACID) -- Dataset

Example study of time series analysis as applied to futures prices forecasting

The AutoCorrelation Integral Drill (ACID) -- Test Bench

Analyze trends and forecast daily revenues.

Prediction of road casualties and evaluate the impact of transformations in Time Series Modeling and Forecasting with ARIMA using the R programming language

A Univariate and Multivariate analysis on National Demand across four years to find patterns of cyclicity and also relationships against energy generation, temperature and wind speed

Codes for calculation of temporal correlations in model-data differences, creating and fitting mathematical models, and cross-validating the fits.

Time Series Forecasting Methods to forecast Daily Post Publications on Medium

Exploratory data analysis using python frameworks🎯| Time Series analysis⌚| Regression Analysis🧬

MATLAB codes for analyzing grid simulations for symptoms of Critical Slowing Down.

Compilation of main codes used in the paper "Heterogeneous antiferroelectric ordering in NaNbO3-SrSnO3 ceramics revealed by direct superstructure imaging"

Time Series Forecasting: City and Resort Hotels Bookings forecasting

Análisis operativo y salud financiera del e-commerce brasileño Olist. Incluye la automatización de la creación de la base de datos en MySQL, validación estadística de estacionalidad (ACF/PACF) y dashboards estratégicos para la optimización logística.

Explored a decade of CPI and BER data using Python and Jupyter notebooks for in-depth time series analysis, forecasting, and error evaluation. Techniques include data cleaning, exploratory analysis, and specialized time series modeling.