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ChatGPT
Apr 29
1. Time: The time component represents the sequence of observations taken at regular intervals.
2. Data points: These are the values of the variable being measured at each time point.
3. Trend: The long-term movement or direction of the time series data, which can be increasing, decreasing, or stable over time.
4. Seasonality: The periodic fluctuations or patterns in the data that occur at regular intervals, such as daily, weekly, monthly, or yearly.
5. Cyclical patterns: Longer-term fluctuations in the data that are not of fixed frequency, such as economic cycles or business cycles.
6. Random or irregular components: Unpredictable fluctuations or noise in the data that cannot be attributed to any specific trend, seasonality, or cyclical pattern.
2. Data points: These are the values of the variable being measured at each time point.
3. Trend: The long-term movement or direction of the time series data, which can be increasing, decreasing, or stable over time.
4. Seasonality: The periodic fluctuations or patterns in the data that occur at regular intervals, such as daily, weekly, monthly, or yearly.
5. Cyclical patterns: Longer-term fluctuations in the data that are not of fixed frequency, such as economic cycles or business cycles.
6. Random or irregular components: Unpredictable fluctuations or noise in the data that cannot be attributed to any specific trend, seasonality, or cyclical pattern.
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Google Gemini AI
Apr 29
Components of a Time Series:
1. Level (Trend): The general upward or downward trajectory of the data over time, capturing the long-term trend.
2. Seasonality: Cyclical patterns that repeat over regular intervals, such as daily, weekly, monthly, or annual cycles.
3. Cyclical: Irregular fluctuations that occur over multiple years, but are not strictly periodic.
4. Irregular (Noise): Random and unpredictable variations in the data that cannot be attributed to any specific cause.
Additional Components (for some time series):
5. Exogenous Events: External shocks or events that significantly impact the time series, such as natural disasters, economic crises, or policy changes.
6. Missing Values: Gaps in the data that may require imputation or estimation.
7. Outliers: Extreme data points that deviate significantly from the rest of the data.
8. Trend Change: Shifts or breaks in the trend, indicating a change in the underlying dynamics.
1. Level (Trend): The general upward or downward trajectory of the data over time, capturing the long-term trend.
2. Seasonality: Cyclical patterns that repeat over regular intervals, such as daily, weekly, monthly, or annual cycles.
3. Cyclical: Irregular fluctuations that occur over multiple years, but are not strictly periodic.
4. Irregular (Noise): Random and unpredictable variations in the data that cannot be attributed to any specific cause.
Additional Components (for some time series):
5. Exogenous Events: External shocks or events that significantly impact the time series, such as natural disasters, economic crises, or policy changes.
6. Missing Values: Gaps in the data that may require imputation or estimation.
7. Outliers: Extreme data points that deviate significantly from the rest of the data.
8. Trend Change: Shifts or breaks in the trend, indicating a change in the underlying dynamics.
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