Overview of 12 Time Series Analysis Techniques

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Time series analysis involves various techniques to analyze and model data that is ordered chronologically. Here are some common time series techniques:

1. Descriptive Analysis:

• Objective: Understand the basic characteristics of the time series.
• Techniques: Plotting time series data, calculating summary statistics, examining trends, and seasonality.

2. Smoothing:

• Objective: Remove noise and highlight underlying trends.
• Techniques: Moving averages, exponential smoothing.

3. Decomposition:

• Objective: Break down a time series into its constituent components (trend, seasonality, residual).
• Techniques: Seasonal decomposition of time series (STL), X-12-ARIMA.

4. Stationarity Testing:

• Objective: Ensure statistical properties of the time series do not change over time.
• Techniques: Augmented Dickey-Fuller (ADF) test, Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test.

5. Autoregressive Integrated Moving Average (ARIMA) Models:

• Objective: Model time series data…