license: mit
language:
- en
metrics:
- mae
- r_squared
- mape
- mse
pipeline_tag: time-series-forecasting
datasets:
- Captain-Slow/Financial_datasets
This repository contains a collection of Time Series Analysis and Forecasting notebooks, with a focus on applications to financial datasets. The objective is to investigate patterns, trends, and explore predictive modeling techniques using both statistical and machine learning methods.
What’s Inside
Exploratory Data Analysis (EDA)
Techniques for visualizing, decomposing, and understanding temporal structures in financial time series.Feature Engineering for Time Series
Lag features, rolling statistics, seasonal indicators, and date-based encodings.Classical Forecasting Methods
- ARIMA / SARIMA
- Facebook Prophet
- Vector Auto Regression
- Arch/Garch for volatility modeling
- Single and Double Exponential Smoothing
- Holt Winters Exponential Smoothing
Machine Learning Approaches
- Random Forests
- XGBoost
- Long Short Term Memory
Model Optimization and Evaluation
Grid-search-cv , Randomized-search-cv, Training with cross-validation, and performance metrics (MAE, RMSE, MAPE).Additional concpets covered Grangers causality test, Parameter selection with AIC , BIC
Datasets
The notebooks primarily work with the following financial datasets:
- Stock price data.
- Commodity Prices.
- Foreign Exchnage rates.
- Inflation rates.
- Cryptocurrency price histories.
- Sales and Revenue datasets