Update README.md

README.md

@@ -0,0 +1,47 @@
+#  Time Series Analysis and Forecasting Notebooks
+
+Welcome to this repository of Jupyter notebooks focused on **time series analysis** and **forecasting**, with applications to **financial datasets**. The goal of this collection is to explore patterns, trends, and predictive modeling techniques using both **statistical** and **machine learning** methods.
+
+---
+
+##  What’s Inside
+
+This repository includes the following:
+
+- **Exploratory Data Analysis (EDA)**  
+  Techniques for visualizing, decomposing, and understanding temporal structures in financial time series.
+
+- **Classical Forecasting Methods**  
+  Models such as:
+  - ARIMA / SARIMA  
+  - Facebook Prophet
+  - Vector Auto Regression
+  - Arch/Garch for volatility modeling
+  
+
+- **Machine Learning Approaches**  
+  Implementation of:
+  - Random Forests for time series regression  
+  - XGBoost for trend and anomaly prediction  
+  - Long Short Term Memory
+
+- **Feature Engineering for Time Series**  
+  Lag features, rolling statistics, seasonal indicators, and date-based encodings.
+
+- **Model Optimization and Evaluation**  
+  Grid-search-cv , Randomized-searhc-cv, cross-validation for time series, and performance metrics (MAE, RMSE, MAPE).
+
+---
+
+## Datasets
+
+The notebooks primarily work with **financial datasets**, such as:
+
+- Stock price data.
+- Commodity Prices.
+- Foreign Exchnage rates.
+- Inflation rates.
+- Cryptocurrency price histories.
+- Sales datasets
+
+