Time Series Analysis And Forecasting

This section covers the foundational concepts in time series analysis, including data characteristics, components, stationarity, and forecasting techniques.

What Is Time Series Data?

Time Series Data refers to a sequence of data points collected or recorded at specified time intervals, representing trends, seasonality, cyclic patterns, and noise.

Components Of Time Series

This section outlines the four main components of time series data: trend, seasonality, cyclic patterns, and irregular variations.

Stationarity In Time Series

Stationarity in time series analysis refers to the statistical properties of a series remaining constant over time, crucial for accurate forecasting.

Autocorrelation And Partial Autocorrelation

This section introduces autocorrelation and partial autocorrelation, crucial tools in time series analysis for identifying lags and model orders.

Classical Time Series Models

This section introduces the classical time series models, including Autoregressive (AR), Moving Average (MA), ARMA, and ARIMA models, essential for forecasting time series data.

Seasonal Models: Sarima And Sarimax

This section covers SARIMA and SARIMAX, models specifically designed to handle seasonality in time series data, where SARIMA processes seasonal components explicitly and SARIMAX extends it by including exogenous variables.

Exponential Smoothing Methods

Exponential smoothing methods are used for forecasting time series data with different patterns.

Time Series Forecasting With Machine Learning

This section discusses how to leverage machine learning techniques for time series forecasting through feature engineering and various algorithms.

Deep Learning For Time Series Forecasting

This section discusses deep learning techniques used for time series forecasting, focusing on RNNs, LSTMs, GRUs, and TCNs.

Evaluation Metrics For Forecasting

This section covers various evaluation metrics used to assess the accuracy of forecasting models in time series analysis.

Common Challenges In Time Series

This section highlights the key challenges encountered in time series analysis, including missing data, outliers, and non-stationarity.

Applications Of Time Series Forecasting

This section discusses various real-world applications of time series forecasting across different sectors.