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Time series forecasting is a crucial application of machine learning and deep learning techniques. It involves predicting future values of a time series based on historical data. With the increasing availability of data and advancements in deep learning, the accuracy of time series forecasting models has significantly improved.

In this article at OpenGenus, we present a list of 21 time series/forecasting project ideas that can help you improve your skills and understanding of deep learning techniques. For each project, we provide a brief description, the dataset used, difficulty level, concepts involved, and a source code link if available on GitHub. These projects can be used to develop skills in time series forecasting and inspire you to explore further in this field.

## 1. Stock Price Prediction using LSTM

A project to predict the future stock price of a company using Long Short-Term Memory (LSTM) neural networks.

- Project title: Stock Price Prediction using LSTM
- Dataset used: Yahoo Finance
- Difficulty level: 3
- Concepts involved: LSTM, Time Series Forecasting
- Source code: https://github.com/lilianweng/stock-rnn

## 2. Sales Forecasting with ARIMA

A project to forecast sales using the Autoregressive Integrated Moving Average (ARIMA) model.

- Project title: Sales Forecasting with ARIMA
- Dataset used: Kaggle Retail Dataset
- Difficulty level: 2
- Concepts involved: ARIMA, Time Series Forecasting
- Source code: https://github.com/Kanishkparganiha/product-sales-forecasting-with-ARIMA

## 3. Energy Demand Forecasting with RNNs

A project to forecast the demand for electricity using Recurrent Neural Networks (RNNs).

- Project title: Energy Demand Forecasting with RNNs
- Dataset used: Kaggle Electricity Demand Dataset
- Difficulty level: 4
- Concepts involved: RNNs, LSTM, Time Series Forecasting
- Source code: https://github.com/spandangandhi/Electricity-Demand-Prediction-through-Neural-Network

## 4. Time Series Anomaly Detection with LSTM

A project to detect anomalies in a time series using LSTM.

- Project title: Time Series Anomaly Detection with LSTM
- Dataset used: Johnson & Johnson (JNJ) daily data from 1985 to 2020
- Difficulty level: 4
- Concepts involved: LSTM, Time Series Forecasting, Anomaly Detection
- Source code: https://github.com/trajceskijovan/Timeseries-anomaly-detection-using-LSTM

## 5. Bitcoin price prediction using ARIMA

A project to predict cryptocurrency prices using ARIMA.

- Project title: Bitcoin price prediction using ARIMA
- Dataset used: Bitcoin Historical Data
- Difficulty level: 4
- Concepts involved: ARIMA, Time Series Forecasting, Cryptocurrency
- Source code: https://github.com/Pradnya1208/Bitcoin-Price-Prediction-using-ARIMA

## 6. Traffic Volume Prediction with LSTM

A project to predict traffic volume using LSTM.

- Project title: Traffic Volume Prediction with LSTM
- Dataset used: Caltrans Performance Measurement System (PeMS)
- Difficulty level: 3
- Concepts involved: LSTM, Time Series Forecasting, Traffic
- Source code: https://github.com/xiaochus/TrafficFlowPrediction

## 7. Predicting Bike Sharing Demand

In this project, you will be predicting the demand for bike sharing using Random Forest algorithm and XGBoost.

- Project title: Predicting Bike Sharing Demand
- Dataset used: Seoul Bike Data
- Difficulty level: 2
- Concepts involved: Random Forest, Time Series Analysis, Data Preprocessing, Data Visualization
- Source code: https://github.com/apoorvaKR12695/Bike-Sharing-Demand-Prediction-

## 8. Air Pollution Forecasting

Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

- Project title: Air Pollution Forecasting
- Dataset used: US embassy in Beijing, China
- Difficulty level: 3
- Concepts involved: LSTM, Time Series Forecasting
- Source code: https://github.com/jyoti0225/Air-Pollution-Forecasting

## 9. Prediction of Solar Power Energy Generation

This project aims to predict solar power energy generation using machine learning models. The dataset used is from the National Renewable Energy Laboratory (NREL) and includes weather data and solar power generation data for a solar photovoltaic (PV) power plant in Alabama, USA.

- Project title: Prediction of Solar Power Energy Generation
- Dataset used: NREL Solar power plant data
- Difficulty level: 3
- Concepts involved: Time series analysis, machine learning, feature engineering, regression
- Source code: https://github.com/juhjoo/Prediction-of-Solar-Power-Energy-Generation

## 10. Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN

This project implements a time series forecasting model for Amazon stock prices using Long Short-Term Memory (LSTM) and Generative Adversarial Network (GAN) models. It includes data preprocessing, model training, and evaluation of the results.

- Project Title: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN
- Dataset used: Amazon stock price data obtained from Yahoo Finance
- Difficulty level: 3
- Concepts involved: Time series forecasting, LSTM, GAN, data preprocessing, model training, evaluation metrics
- Source code: https://github.com/deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-

## 11. Cryptocurrency Price Prediction

This project aims to predict the prices of cryptocurrencies like Bitcoin and Ethereum using various machine learning and deep learning models. The project uses data from cryptocurrency exchanges to train and test the models.

- Project title: Cryptocurrency Price Prediction
- Dataset used: Cryptocurrency market data from various exchanges
- Difficulty level: 4
- Concepts involved: Time series analysis, feature engineering, machine learning, deep learning
- Source code: https://github.com/abhinavsagar/cryptocurrency-price-prediction

## 12. Boston Airbnb Price Prediction

This project involves building a machine learning model to predict the prices of Boston Airbnb listings based on various features. The dataset used in this project is the Boston Airbnb Open Data, which contains detailed information about the Airbnb listings in Boston, such as the type of room, neighborhood, and host information.

- Project title: Boston Airbnb Price Prediction
- Dataset used: Boston Airbnb Open Data
- Difficulty level: 3
- Concepts involved: Data preprocessing, feature engineering, machine learning, regression, evaluation metrics
- Source code: https://github.com/shyhn/boston-airbnb

## 13. LSTM Load Forecasting

This project aims to predict the electricity load demand in the future using LSTM neural networks. The dataset used is the Global Energy Forecasting Competition 2012 (GEFCom2012) load forecasting dataset. The goal is to provide an accurate forecast of the electricity load to assist in better energy management and planning.

- Project title: LSTM Load Forecasting
- Dataset used: GEFCom2012 load forecasting dataset
- Difficulty level: 3
- Concepts involved: LSTM, time series forecasting, neural networks
- Source code: https://github.com/dafrie/lstm-load-forecasting

## 14. Rainfall analysis of Maharashtra

Rainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.

- Project Title: Rainfall prediction of Maharashtra
- Dataset used: Various sources
- Difficulty level: 4
- Concepts involved: ARIMA, LSTM, ANN
- Source code: https://github.com/Abhishekmamidi123/Time-Series-Forecasting

## 15. Global Temperature Change Prediction

A Data Science project that uses an ARIMA model for Time Series Forecasting, to predict the temperature of any given city across a specific time period.

- Project title: Global Temperature Change Prediction
- Dataset used: Kaggle
- Difficulty level: 3
- Concepts involved: Time Series Analysis, Data Visualization, Machine Learning
- Source code: https://github.com/My-Machine-Learning-Projects/Global-Temperature-Change-Prediction

## 16. Predicting Wind Speed and Direction

Predict wind speed and direction for a location using historical weather data. The project involves time series forecasting techniques such as LSTM, GRU, and Time Series Regression.

- Project title: Predicting Wind Speed and Direction
- Dataset used: National Oceanic and Atmospheric Administration (NOAA) dataset, Weather Underground dataset
- Difficulty level: 3/5
- Concepts involved: LSTM, GRU, Time Series Regression

## 17. Predicting Hospital Admissions

Predict hospital admissions for a hospital or a region using historical hospital admission data. The project involves time series forecasting techniques such as LSTM, GRU, and Time Series Regression.

- Project title: Predicting Hospital Admissions
- Dataset used: Healthcare Cost and Utilization Project (HCUP) dataset, Medicare dataset
- Difficulty level: 3/5
- Concepts involved: LSTM, GRU, Time Series Regression

## 18. Forex trading strategy

This project involves creating a trading strategy based on time series data of previous forex prices. You can use datasets such as the OANDA API or the Yahoo Finance API to get forex data.

- Project title: Forex Trading Strategy
- Dataset used: OANDA API, Yahoo Finance API
- Difficulty level: 4
- Concepts involved: Time series forecasting, LSTM, deep learning, algorithmic trading

## 19. Real-time Stock Price Prediction

Develop a model that predicts the stock price of a company in real-time using historical stock data and relevant news articles.

- Dataset used: Historical stock data and news articles
- Difficulty level: 4
- Concepts involved: Natural Language Processing (NLP), Time Series Analysis, Deep Learning

## 20. Predicting Solar Energy Output

Develop a model that predicts the output of solar energy systems based on historical energy production data and relevant environmental factors.

- Dataset used: Historical solar energy production data and weather data
- Difficulty level: 3
- Concepts involved: Time Series Analysis, Deep Learning

## 21. Forecasting Airline Passenger Traffic using ARIMA Models

This project involves forecasting airline passenger traffic using ARIMA models. The dataset can be obtained from the Federal Aviation Administration (FAA) or the Bureau of Transportation Statistics (BTS), and the difficulty level is intermediate. Concepts involved include data preprocessing, time series analysis, and ARIMA models.

Project title: Forecasting Airline Passenger Traffic using ARIMA Models

Dataset used: Airline passenger traffic data from FAA or BTS

Difficulty level: 3/5

Concepts involved: Data preprocessing, time series analysis, ARIMA models