Define “Time Series Analysis”

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Series analysis can usually be performed in two domains – time domain and frequency domain.
Time series analysis is the method where the output forecast of a process is done by analyzing the data collected in the past using techniques like exponential smoothening, log-linear regression method, etc.

Time series analysis is a statistical technique used to analyze and interpret data points collected over time. It involves studying the patterns, trends, and behaviors that emerge from sequential data observations. The primary objective of time series analysis is to understand and predict future values based on past observations.

Key components of time series analysis include:

  1. Data Collection: Gathering data points at regular intervals over time. This could be daily, weekly, monthly, etc.
  2. Data Visualization: Plotting the time series data to identify patterns, trends, and anomalies visually. Common visualizations include line plots, scatter plots, and histograms.
  3. Descriptive Analysis: Examining basic statistics such as mean, median, variance, and standard deviation to understand the central tendency and variability of the data.
  4. Time Series Decomposition: Decomposing the time series into its constituent components such as trend, seasonality, cyclicality, and random noise.
  5. Modeling: Building mathematical models to represent the underlying structure of the time series data. This could involve techniques such as autoregression (AR), moving average (MA), autoregressive integrated moving average (ARIMA), seasonal decomposition of time series (STL), and more advanced methods like exponential smoothing and machine learning algorithms.
  6. Forecasting: Using the developed models to predict future values of the time series data. Forecasting techniques range from simple methods like extrapolation to sophisticated machine learning algorithms such as neural networks and support vector machines.
  7. Evaluation: Assessing the accuracy and reliability of the forecasting models through various metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and forecast skill measures.

Time series analysis is widely used in various fields including finance, economics, weather forecasting, signal processing, and engineering for making informed decisions, identifying trends, detecting anomalies, and optimizing processes.