Today I learnt about Time Series analysis, and I’m going to share some key points on it,
Analyzing and modeling data points gathered or recorded over time is known as time series analysis. Numerous disciplines, including finance, economics, biology, engineering, and environmental science, heavily rely on it. Time series data usually consist of observations that are recorded at consecutive points in time and have a temporal ordering. The following are some essential ideas and procedures for time series analysis,
Time Series Components:
Trend: The data’s long-term movement or orientation.
Seasonality: Recurring cycles or patterns that happen on a set schedule.
Cyclical patterns are seasonality-like but have erratic durations.
Unpredictable variations that persist after trend and seasonality are eliminated are known as random (residual) fluctuations.
Illustration:
To visually examine trends, seasonality, and other patterns, plot the time series data.
recognizing anomalies and outliers.
Steadiness:
Many time series models make the assumption of stationarity, which is the absence of time-varying statistical characteristics in the data.
One can use methods like differencing to make a time series stationary.
Partial autocorrelation and autocorrelation
The relationship between an observation and its lag values can be determined with the aid of the autocorrelation function (ACF) and partial autocorrelation function (PACF).
Decomposition of Time Series:
- Time Series Decomposition:
- Separating the time series into trend, seasonality, and residual components to facilitate analysis.
- Modeling:
- ARIMA Models (AutoRegressive Integrated Moving Average): Combines autoregression, differencing, and moving averages to capture different components of a time series.
- Exponential Smoothing State Space Models (ETS): A family of forecasting models that includes simple exponential smoothing, double exponential smoothing, and triple exponential smoothing (Holt-Winters method).
- Seasonal-Trend decomposition using LOESS (STL): A robust method for decomposing time series.
- Model Evaluation:
- Splitting the data into training and testing sets to evaluate the model’s performance.
- Metrics such as Mean Squared Error (MSE) or Mean Absolute Error (MAE) are commonly used for evaluation.
- Forecasting:
- Using the fitted model to make future predictions.
- Visualizing and interpreting forecast results.
Popular tools for time series analysis include programming languages like Python and R, and libraries such as pandas, statsmodels, and scikit-learn in Python, and the forecast package in R. Time series analysis is a vast and evolving field, and the choice of methods depends on the characteristics of the specific time series data and the goals of analysis.
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