Nirva-Patel / Time-Series-Forecasting

This project aims to forecast gold prices using time series techniques. It includes data preprocessing, model training, and evaluation of methods like ARIMA, SARIMA, RNN,LSTM,GRU etc. to identify the best-performing model. The repository contains code, data workflows, and model comparisons.

📈 Gold Price Forecasting using Time Series Models

This project aims to forecast gold prices using advanced time series techniques. It includes data preprocessing, model training, and evaluation of methods such as ARIMA, SARIMA, LSTM, GRU and others to identify the best-performing approach for accurate predictions.

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📊 Project Overview

The goal is to explore and compare different time series models to forecast gold prices based on historical data. The repository contains all necessary code, workflows, and visualizations to support reproducibility and performance benchmarking.

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🧰 Technologies & Libraries Used

• Python
• Pandas & NumPy
• Matplotlib & Seaborn
• Statsmodels (ARIMA/SARIMA)
• TensorFlow / Keras (LSTM) (GRU)
• Scikit-learn

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🛠️ Features

• Time series data preprocessing & transformation
• Implementation of statistical and deep learning models
• Model evaluation using standard metrics
• Visualization of results and forecast performance
• Easy-to-follow and modular code structure

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