What is Time Series?
A time series is a series of observations occurring in a temporal order or, in other words, a series of data points in a time order. When a variable is defined at all points in time, the time series is said to be continuous. This includes, for example, the temperature, commodity prices in an open market, or the velocity of a satellite.
In practical analyses, a time series is discrete. It is composed of observations or data points, captured at successive equally spaced points in time. Most of discrete time series are based on aggregation of timely observations over a period.
For example, weekly sales, stock daily closing price, hourly average temperature, monthly salaries, consumer confidence index measures may form a discrete time series. To analyze time series, several advanced techniques and methodologies have been proposed and have evolved over time
Time Series or Forecasting?
Time series analysis is the process of using statistical and machine learning techniques to model and explain a time-dependent series. Time series analyses include two steps: Time Series Analysis, for modelling and explaining time related relationships and Forecasting, for predictions based on the past observations..
Time series analyses and forecasting assume data points are observed at regular time intervals.
Time Series involves modelling and explaining the following:
- trend or long-term movement
- cyclical components
- fluctuations about trend of greater or less regularity
- seasonal components
- residual, irregular or random effects
Forecasting uses time series analysis to:
- mathematically and statistically construct and describe a prediction system
- explain behaviors with overlay, regressor or explanatory variables
- assess scenarios (what if?)
Time Series Analysis uses several methodological approaches, including Auto Regressive (AR), Moving Average (MA), Auto Regressive Moving Average (ARMA), Auto Regressive Integrated Moving Average (ARIMA), Simple or Double Exponential Smoothing (SES/DES), Holt-Winters, ARIMA with explanatory variables or transfer functions (ARIMAX), and Structural models or unobserved components models (UCM), with explanatory variables.
With a UCM approach, AroniSmart™ Time Series module has adopted a machine learning/data mining methodology with a cutting edge, yet intuitive, approach. The graphical user interface shows the graphs generated (see examples).
With a UCM approach, AroniSmart Time Series module has adopted a machine learning/data mining methodology with a cutting edge, yet intuitive, approach. The graphical user interface shows the graphs generated (see examples).More in store.
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