This repository was created to host my solutions to the official Alteryx weekly challenges.

A time-series companion package to healthyR

Population Prediction forecasts the Haggis population on a mountain. Ecologists have recorded the population over five years and have satellite estimates. The goal is to predict the true population 12 months ahead using machine learning and time series analysis techniques. This project is for the COM6509 - Machine Learning and Adaptive Intelligence

Welcome to the Macroeconomic Forecasting and Causality Analysis repository! Here, we use RATS Econometrics software to analyze and forecast key macroeconomic variables such as Consumer Confidence, Housing Prices, Federal Funds Rate, and Government Job Openings, exploring their interrelationships, mainly through ARIMA and VAR models.

LSTM-ARIMA with Attention and multiplicative decomposition for sophisticated stock forecasting.

Stonks Rabbi is a streamlit-based application that uses the Yahoo Finance API to visualize and analyze stock trends, patterns, and performance over its listed time period. The metadata is handled through pymongo, the frontend is on streamlit, and autoARIMA, pandas, and matplotlib are used for data analytics and visualization.

Forecasting the rate at which atmospheric CO2 levels are increasing globally by performing a time series analysis on the atmospheric concentration of CO2 based on data from an observatory located in Mauna Loa, Hawaii.

The set of functions used for time series analysis and in forecasting.

detailed and comprehensive time-series analysis using python (includes ARIMA and SARIMA)

Time series forecasting for store sales with ARIMA

Forecast Bitcoin daily closing prices using a Python repository featuring regression and time series models. From Linear and Polynomial Regression to ARIMA, gain insights into cryptocurrency trends. Visualize historical data, evaluate models with key metrics, and analyze residuals for validation

Analyzes avocado prices from 2015-2017 using ARIMA for forecasting; it captures trends and seasonality to forecast early 2018 prices, with EDA revealing price distributions and regional preferences. Outlier detection ensures data accuracy, considering consumer preferences and production cycles.

Analysis and Forecast of Global Mean Sea Level Change due to Global Warming

Implementation and comparison of ARIMA models to forecast Olist revenue for the next 14 days.

Implemented Airline Passengers Traffic Forecasting using ARIMA Model for next 5 years.

GDP is the most widely used measure of the level of economic activity. However, the GDP is usually published after the date of registration of the information. On the other hand, various investigations show that the demand for electrical energy is a coincident indicator of GDP.

This project aims to investigate temperature changes over time and predict future temperature patterns on a regional and global scale. We employ time series forecasting methods, including neural networks, ARIMA, and SARIMAX, using the GISTEMP v4 dataset from NA

Explore CSST 104 Advanced Machine Learning repo! Python on Google Colab for tasks. Real-life examples show concepts like linear regression, predictive analysis.

This project aims to predict gold prices using various time series forecasting techniques. The dataset consists of monthly gold futures data over the last ten years. The primary methods used in this analysis include ARIMA, Error Trend Seasonal (ETS) models, and Exponential Smoothing techniques. The forecast horizon is set for the next two years.

It is a TimeSeries analysis with forecast using ARIMA methods.