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Time Series Analysis

  • Time series data is a type of data that is collected and recorded at regular intervals over a period of time. Time series analysis is the process of analyzing this data in order to identify trends and patterns, forecast future values, and make informed decisions.

  • One common application of time series analysis is in the financial industry, where stock prices are collected and analyzed over time. By analyzing stock prices over a period of time, analysts can identify trends and patterns in the data, predict future stock prices, and make informed investment decisions. Time series analysis techniques such as trend analysis, seasonality analysis, and autocorrelation analysis can be used to analyze stock prices and make these predictions.

  • This repository contains a variety of notebooks for time series analysis using stock prices. These notebooks make use of a range of algorithms including autoregressive (AR), autoregressive moving average (ARMA), moving average (MA), autoregressive integrated moving average (ARIMA), seasonal autoregressive moving average (SARMA), seasonal autoregressive integrated moving average (SARIMA), auto Arima, and generalized autoregressive conditional heteroskedasticity (GARCH) for analyzing and predicting stock prices.

  • The repository includes a range of projects that utilize these algorithms to analyze and predict stock prices, including a project that uses ARMA and ARIMA algorithms to analyze and forecast stock prices for a specific company. This project includes detailed reports and graphs that allow users to see the accuracy of the predictions and the impact of different variables on the outcomes.

  • The four main stock price used for the projects include : FTSE, DAX, NIKKEI and SPX.

  • In addition to these projects, the repository also includes a project that utilizes the Facebook Prophet library to forecast stock prices for a specific company. This project includes charts that allow users to easily see the accuracy of the predictions and the impact of different variables on the outcomes.

  • Overall, this repository contains a range of projects that demonstrate the power and versatility of different time series analysis algorithms and libraries in analyzing and predicting stock prices.