Implementation of Time Series

Current repository depicts R usability for time series modeling. Number of scripts represents preprocessing time series, modeling AR, MA, ARIMA with seasonality, ARCH, GARCH, VAR, VECM including statistical testing process and robust check.

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 project presents a dual-phase analysis leveraging Python for two disparate datasets. In the initial segment, an exhaustive time series analysis is conducted on historical weather data sourced from Dublin Airport.

This project deals with Time Series Problem to predict the number of passengers in a train over the next 7 months

Time Series Analysis of CO2_emission. Study from exercise E3 of 'Physics and Finance'

Predicting Monthly Sales of Perrin Freres Champagne by ARIMA & SARIMAX model.

Forecasting_Airline_Passengers_Seats

Time series preprocessing. (G)ARCH, VECM, VAR modeling on stock data.

EDA performed on timeseries data and different plots to get insights from data visualizations