Time Series Analysis of CO2_emission. Study from exercise E3 of 'Physics and Finance'
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Updated
Aug 9, 2024 - Jupyter Notebook
Time Series Analysis of CO2_emission. Study from exercise E3 of 'Physics and Finance'
MATLAB implementation of the DF-GLS unit root test of Elliott, Rothenberg & Stock (1996), with 3 optimal lag-length selection methods (SIC, MAIC, Sequential-t) for selecting the lagged terms in the underlying ADF regression.
Material for the course "Time series analysis with Python"
Data Science: Machine Learning analysis of B2B website Visits and Purchase Patterns
'XTARIMAU': module to find the best [S]ARIMA[X] models in heterogeneous panels with the help of arimaauto
'ARIMAAUTO': module to find the best ARIMA model with the help of a Stata-adjusted Hyndman-Khandakar (2008) algorithm
Analysis of Skewness and Kurtosis in Stock Return data and their Transformations
Resampling procedure for weakly dependent stationary observations.
Using Python for comprehensive data analyses and machine learning, alongside Tableau for advanced data visualization, for a German company seeking to expand their customer base.
R finance guide - Algotrading101
Common vulnerabilities and exposure.
Machine Learning in Scikit-Learn and TensorFlow
Time series preprocessing. (G)ARCH, VECM, VAR modeling on stock data.
This project builds a time series model that forecasts a 3-year industrial production of electic and gas utilities in US.
package for modelling Time Series Processes as locally stationary processes
Filters (kalman, hodrick-prescott, moving average) together with comparison and sensitivity analysis (in notebook filters_with_parameters)+var analysis and granger causality test. Test for random walk (CE currencies using yfinance API)
Predict the apple stock market price for next 30 days. There are Open, High, Low and Close price has been given for each day starting from 2012 to 2019 for Apple stock.
Stochastic simulations of population abundance with known component density feedback on survival to test for ability to return ensemble feedback signal
Modelo de machine learning con series temporales que predice la cantidad de taxis para la próxima hora.
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.
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