Development of several time series forecasting models

I am looking for a qualified data processing specialist who is proficient in Python and machine learning. The project focuses on forecasting cyclical economic trends in the stock market. The goal is to identify and predict market cycles, such as bull and bear markets, using advanced data analysis and machine learning methods. Historical data from 2010 to 2023 should be used as the basis for the analysis.

The scope of work includes:

- Collecting historical stock price data (e.g., S&P 500 or similar) using sources such as Yahoo Finance and Alpha Vantage.

- Cleaning and preparing data for analysis.

- Identifying patterns, seasonality, and trends in the data, visualizing the correlation between stock prices and economic indicators.

- Implementing and comparing different time series forecasting models, such as ARIMA, SARIMA, and Prophet. Using machine learning models (e.g., Random Forest, Gradient Boosting) and deep learning models (e.g., LSTM, GRU) to forecast market cycles.

- Evaluating models using metrics such as RMSE, MAPE, and R-squared.

The final deliverables are:

- a detailed report

- a set of forecasting models

- a minimal user interface (I will describe)

 The ideal candidate has experience with Python machine learning libraries such as TensorFlow and Scikit-learn, as well as experience in data analysis and developing forecasting models.

Requirements:

Proficiency in Python and libraries such as Pandas, NumPy, Matplotlib/Seaborn, and Scikit-learn.

Experience with time series forecasting methods (e.g., ARIMA, LSTM, Prophet).

Familiarity with financial data and economic indicators is a plus.

Strong skills in data preprocessing, feature engineering, and model evaluation.

Ability to provide well-documented code and clear explanations.

Deadline -- 2 weeks. It is not for commercial use, so it does not need to be complicated, just functional.

Budget is negotiable.