Machine Learning Time Series Analysis aims to extract meaningful insights from time-series data by identifying patterns, trends, and relationships within the data.

ARIMA is a widely used time series model that combines autoregressive, integrated, and moving average components to capture the dynamics of a time series.

Differencing is a technique used to remove trend and seasonality from a time series, making it stationary. This is important for many time series analysis techniques to work effectively.

Stationarity is a fundamental concept in time series analysis. A time series is considered stationary if its statistical properties, such as mean, variance, and autocorrelation, remain constant over time.

MAE, RMSE, and MSE are all commonly used metrics for evaluating the performance of time series forecasting models. They measure the difference between the actual values and the predicted values.

The Box-Jenkins methodology is a systematic approach for building and validating time series models. It involves identifying the appropriate model, estimating its parameters, and checking its adequacy.

Cross-validation is a technique used to evaluate the performance of a time series forecasting model on unseen data. It helps in estimating the generalization error, selecting optimal hyperparameters, and detecting overfitting.

RNNs are a class of deep learning models that are specifically designed to handle sequential data, making them well-suited for time series forecasting.

Seasonality refers to the repeating pattern of fluctuations in a time series that occurs over a specific period, such as daily, weekly, or yearly.

Supervised learning involves training a model on labeled data, where the output variable is known, while unsupervised learning involves training a model on unlabeled data, where the output variable is unknown. Supervised learning is often used for forecasting, while unsupervised learning can be used for anomaly detection. Supervised learning models are generally more complex than unsupervised learning models.

Feature engineering involves transforming and extracting relevant features from the raw time series data. This can improve the performance of time series forecasting models by making the data more informative and reducing the dimensionality of the data.

OCSVM, LOF, and Isolation Forest are all commonly used methods for detecting anomalies in time series data. These methods identify data points that deviate significantly from the normal behavior of the time series.

Cointegration refers to a long-run equilibrium relationship between two or more time series variables, meaning that they tend to move together in the long run.

LSTM, GRU, and CNN are all commonly used deep learning architectures for time series forecasting. LSTM and GRU are specifically designed for sequential data, while CNN can be used for time series data with spatial or temporal dependencies.

The primary goal of time series forecasting is to use historical data to make predictions about future values of a time series.