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Time Series Data Analysis

Time Series Data Analysis is a statistical technique that refers to the sequentially ordered set of observations for time periods. The significant role of time series analysis predicting future values of the time series variables based on historical datasets. A time series contains sequential data points mapped at certain successive time duration, and it incorporates the techniques that attempt to suspect a time series in terms of understanding either the fundamental concept of the data points in the time series or making predictions. Time-series data analysis aims to predict future values based on previous values observed at regular time intervals. Data types of time series variables are stationary: statistical moments that do not change with time, and non-stationary: statistical properties that change with time. Moving Average (MA), Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing Method(ESM) are the methods used in machine learning modeling for time series data analysis. Machine learning algorithms used in time series forecasting are Multi-Layer Perceptron (MLP), Bayesian Neural Network (BNN), Radial Basis Functions (RBF), Generalized Regression Neural Networks (GRNN), K-Nearest Neighbor regression (KNN), CART regression trees (CART), Support Vector Regression (SVR) and Gaussian Processes (GP). Financial Analysis and Forecasting, Stock Market and Trends Analysis, Inventory analysis, Census Analysis, Yield prediction, Sales forecasting, Weather prediction, Bio-informatics, Anamoly detection, Blood pressure tracking, and Heart rate monitoring are the most popular applications of time series data analysis. Machine learning advances in time series forecasting is high dimensional supervised machine learning model with both linear and nonlinear alternatives, Highly Scalable Autonomous Time Series Analysis, Time-series classification for new-generation Earth observation satellites, and time series analysis for modeling of transmission of diseases.