A time series is a sequence of data points measured or recorded at successive points in time. Time series models are essential tools for analyzing and forecasting such data.
Types of Time Series
They include :-
- Seasonal Time Series:
Characteristics: Displays repeating patterns or cycles at regular intervals.
Example: Retail sales often have seasonal patterns, such as increased sales during holidays.
- Cyclical Time Series:
Characteristics: Displays fluctuations around the trend, but the period is not fixed.
Example: Economic indicators can show cyclical patterns over several years.
- Seasonal Time Series:
Characteristics: Displays repeating patterns or cycles at regular intervals.
Example: Retail sales often have seasonal patterns, such as increased sales during holidays.
- Trended Time Series:
Characteristics: Exhibits a long-term increase or decrease over time.
Example: Stock prices generally show a trend over an extended period.
- Moving Average Time Series:
Characteristics: Future values are dependent on past forecast errors.
Example: Sales data might be influenced by past forecasting errors.
Why Model Time Series
Time series modelling is useful for:
Forecasting future values.
Understanding the underlying forces and structure that produced the observed data.
Monitoring and anomaly detection.
Some common time series models are:-
- Autoregressive Integrated Moving Average (ARIMA):
Description: ARIMA is a widely used model that combines autoregressive, differencing, and moving average components to capture different aspects of time series data.
Use Case: Suitable for data with a clear trend and seasonality.
- Seasonal-Trend decomposition using LOESS (STL):
Description: STL is a decomposition method that separates a time series into its trend, seasonality, and remainder components.
Use Case: Useful for time series with pronounced seasonality and trend.
- Exponential Smoothing State Space Models (ETS):
Description: ETS models capture error, trend, and seasonality components to make forecasts.
Use Case: Suitable for time series data with varying levels of trend and seasonality.
- Prophet:
Description: Developed by Facebook, Prophet is designed for forecasting time series data with daily observations that display patterns on different time scales.
Use Case: Effective for datasets with strong seasonal patterns and holidays.
- SARIMA (Seasonal ARIMA):
Description: An extension of the ARIMA model that includes seasonality components.
Use Case: Appropriate for time series data with both trend and seasonality.
- ARIMA with Exogenous Variables (ARIMAX):
Description: ARIMAX extends the ARIMA model to incorporate external variables that may influence the time series.
Use Case: Useful when additional factors impact the time series data.
How to Build Time Series Models in Python
1. Import the necessary libraries
The main libraries one needs are pandas for data manipulation, Numpy for numerical processing, Matplotib for visualization and Sklearn for modelling
2. Load and explore your time series data
Use Pandas to load the data into a Dataframe. Check the datatypes and summary statistics. Plot the time series to visualize the trend, seasonality and noise.
*3. Split into train and test sets. *
Use train_test_Split to split the data into training and testing sets. A common split is 70%. For training and 30% for testing.
*4. Choose a model. *
The most common time series models are:- Arima, Sarima and neural networks into bracket (LSTM). These are statistical models that account for seasonality and trends.
*5. Train and evaluate the model. *
Fit your model on the training set. Make predictions for the testing set and evaluate the accuracy using mean_squared_error and R2_score. Tune the model hyperparameters to improve performance.
6. Make forecast.
Use the final model to make predictions for future time points. For example, you can focus the next 12 months. Evaluate how the forecast match up to the new data as it becomes available.
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