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https://github.com/saritaphd/basic-understanding-of-forecasting---time-series

This repository provides detailed instructions on how to perform time series forecasting using various models and techniques.
https://github.com/saritaphd/basic-understanding-of-forecasting---time-series

python timeseries-analysis timeseries-forecasting

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This repository provides detailed instructions on how to perform time series forecasting using various models and techniques.

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README 

        
# Time Series - Forecasting

## Table of Contents

- Introduction

- Prerequisites

- Getting Started

- Data Preparation

- Exploratory Data Analysis (EDA)

- Time Series Forecasting Models

- Classical Models

- Machine Learning Models

- Deep Learning Models

- Evaluation Metrics

- Model Selection and Hyperparameter Tuning

- Deployment

- Conclusion



## 1. Introduction

Welcome to the Time Series Forecasting project! This readme file provides detailed instructions on how to perform time series forecasting using various models and techniques. Time series forecasting is a crucial task in many domains, such as finance, weather forecasting, sales prediction, and more.



In this project, we will cover data preparation, exploratory data analysis, various forecasting models, evaluation metrics, model selection, hyperparameter tuning, and deployment.



## 2. Prerequisites

Before you start with time series forecasting, ensure you have the following prerequisites:



- Python 3.x installed

Jupyter Notebook (for better code organization)

Libraries: NumPy, pandas, Matplotlib, scikit-learn, statsmodels, TensorFlow (for deep learning models), and any other specific libraries for your chosen models.

## 3. Getting Started

- Data Preparation

To begin, you need historical time series data. Ensure your data is properly formatted with a timestamp and a target variable. You may need to clean the data, handle missing values, and perform feature engineering.



- Exploratory Data Analysis (EDA)

Exploratory Data Analysis is essential to understand your time series data. Visualize the data, identify trends, seasonality, and any outliers. This will guide your model selection and feature engineering.



## 4. Time Series Forecasting Models

There are various time series forecasting models to choose from. We categorize them into three groups:



## Classical Models

- ARIMA (AutoRegressive Integrated Moving Average): A traditional statistical method.

- Exponential Smoothing: A family of methods that includes Holt-Winters and other variants.

- Prophet: A forecasting tool from Facebook designed for daily observations with strong seasonal patterns.

- TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components): A more advanced forecasting method.

- Machine Learning Models

- Linear Regression: A simple regression model applied to time series data.



## Deep Learning Models

- Recurrent Neural Networks (RNNs): Suitable for sequence data, including time series.

- Long Short-Term Memory (LSTM) Networks: A type of RNN with improved memory capabilities.

- Gated Recurrent Units (GRUs): Another RNN variant that's efficient for sequential data.

## 5. Evaluation Metrics

When evaluating your models, consider using the following metrics:



- Mean Absolute Error (MAE)

- Mean Squared Error (MSE)

- Root Mean Squared Error (RMSE)

- Mean Absolute Percentage Error (MAPE)

- AIC/BIC (for classical models)

- R-squared (for machine learning and deep learning models)

## 6. Model Selection and Hyperparameter Tuning

Select the best model based on evaluation metrics. Conduct hyperparameter tuning if applicable. This may involve grid search, random search, or Bayesian optimization.



## 7. Deployment

Once you have a well-performing model, you can deploy it for real-time or batch forecasting, depending on your use case. This can be done using cloud services, APIs, or embedding the model in your application.



## 8. Conclusion

Time series forecasting is a powerful tool for predicting future trends based on historical data. This readme provides an overview of the process, but keep in mind that it's a complex and iterative task. Experiment with various models and techniques to find the best solution for your specific use case.