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---
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 |