---
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