A sequence of data points collected or recorded at specific time intervals, used for forecasting.

The process of predicting future values based on historical data, often using statistical or machine learning models.

AutoRegressive Integrated Moving Average; a popular statistical method for time series forecasting.

Long Short-Term Memory; a type of recurrent neural network effective for sequence prediction tasks.

Techniques used to clean and prepare raw data for analysis, ensuring quality and consistency.

Long-term movements in time series data, indicating a general direction over time.

Regular patterns or fluctuations in data that occur at specific intervals, such as monthly or quarterly.

Long-term fluctuations in time series data that are not fixed in frequency or duration.

Quantitative measures used to assess the performance of forecasting models, such as RMSE and MAE.

Root Mean Square Error; a commonly used metric to measure the accuracy of a forecasting model.

Mean Absolute Error; another metric for evaluating forecasting accuracy, focusing on average errors.

The graphical representation of information and data, helping to communicate insights effectively.

A widely used Python library for creating static, interactive, and animated visualizations.

A Python data visualization library based on Matplotlib, providing a high-level interface for drawing attractive graphics.

Data points that are not recorded or are unavailable, requiring specific handling techniques.

Data points that differ significantly from other observations, potentially skewing analysis.

The process of scaling data to a standard range, improving model performance and convergence.

Transforming features to have a mean of zero and a standard deviation of one, often used in machine learning.

A comparison of model forecasts against actual observed values to assess model performance.

An interactive visualization tool that displays key metrics and insights for stakeholders.

Predicting future stock prices using historical data and various forecasting techniques.

Practical recommendations derived from data analysis that can inform decision-making.

Evaluating multiple models or datasets to determine the best-performing option.

The process of making informed investment choices based on data analysis and forecasting.

Methods and algorithms used in artificial intelligence to analyze data and make predictions.

Key findings and recommendations derived from analysis that help investors make informed decisions.