Time Series Tutorial
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- Time Series - Discussion
- Time Series - Useful Resources
- Time Series - Quick Guide
- Time Series - Further Scope
- Time Series - Applications
- Time Series - Error Metrics
- Time Series - LSTM Model
- Time Series - Prophet Model
- Time Series - Walk Forward Validation
- Time Series - Exponential Smoothing
- Time Series - Variations of ARIMA
- Time Series - ARIMA
- Time Series - Moving Average
- Time Series - Auto Regression
- Time Series - Naive Methods
- Time Series - Parameter Calibration
- Time Series - Modeling
- Data Processing & Visualization
- Time Series - Python Libraries
- Time Series - Programming Languages
- Time Series - Introduction
- Time Series - Home
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Time Series - Modeling
Time Series - Modepng
Introduction
A time series has 4 components as given below −
Level − It is the mean value around which the series varies.
Trend − It is the increasing or decreasing behavior of a variable with time.
Seasonapty − It is the cycpc behavior of time series.
Noise − It is the error in the observations added due to environmental factors.
Time Series Modepng Techniques
To capture these components, there are a number of popular time series modelpng techniques. This section gives a brief introduction of each technique, however we will discuss about them in detail in the upcoming chapters −
Naïve Methods
These are simple estimation techniques, such as the predicted value is given the value equal to mean of preceding values of the time dependent variable, or previous actual value. These are used for comparison with sophisticated modelpng techniques.
Auto Regression
Auto regression predicts the values of future time periods as a function of values at previous time periods. Predictions of auto regression may fit the data better than that of naïve methods, but it may not be able to account for seasonapty.
ARIMA Model
An auto-regressive integrated moving-average models the value of a variable as a pnear function of previous values and residual errors at previous time steps of a stationary timeseries. However, the real world data may be non-stationary and have seasonapty, thus Seasonal-ARIMA and Fractional-ARIMA were developed. ARIMA works on univariate time series, to handle multiple variables VARIMA was introduced.
Exponential Smoothing
It models the value of a variable as an exponential weighted pnear function of previous values. This statistical model can handle trend and seasonapty as well.
LSTM
Long Short-Term Memory model (LSTM) is a recurrent neural network which is used for time series to account for long term dependencies. It can be trained with large amount of data to capture the trends in multi-variate time series.
The said modelpng techniques are used for time series regression. In the coming chapters, let us now explore all these one by one.
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