The Complete Guide to Time Series Models

What is a time series model

It is a set of data points ordered in time, where time is the independent variable and the goal is to make a forecast for the future.

Time series models are statistical and mathematical tools used to analyze, process and make forecasts from time series data.

Characteristics of Time Series Data

• Stationarity: A time series is said to be stationary if its statistical properties do not change over time,i.e, it has a constant mean and variance and its covariance is independent of time.

From the above image, we see that the process is stationary.

• Seasonality: It refers to periodic fluctuations. For instance, power consumption is normally high during the day and low at night.

• Trend: The long-term movements or direction of the data.

Components of Time Series Data

• Seasonal Component: There are constant variations that happen frequently, such as daily, monthly, or yearly.
• Random Noise Component: It is the unpredictable or rather irregular variation of data that cannot be attributed to any specific pattern.
• Trend Component: Long term movement in the data can either be upward or downward.

Types of Time Series Models

• Prophet: A forecasting model developed by Facebook that is especially designed for time series data with seasonality and trend.
• Exponential Smoothing: It belongs to a group of forecasting models that project future values using a weighted average of historical data.
• Autoregressive Integrated Moving Average(ARIMA): It is a linear model that combines autoregression, differencing, and moving averages to create a flexible and robust forecasting model.
• Seasonal Autoregressive Integrated Moving Average(SARIMA): It is an expansion of the ARIMA model that takes seasonality in the data into consideration.
• Vector Autoregression(VAR): It is a model that is quite suitable for multivariate time series analysis. The model describes the relationships between multiple time series variables.

How to Build a Time Series Model.

1. Prepare the data: This is the initial step that include, loading the data, cleaning data, removing outliers and transforming the data to make the stationary.

2. Identifying the model parameters: It involves using statistical models to estimate the parameters of the chosen time series model.

3. Evaluate the model: This involves checking the performance of the model based on the dataset provided.

4. Using the model to make predictions: After the model has been evaluated and its fit for use, it can be used to make predictions about future values of the data.

Conclusion

For the analysis and forecasting of time-based data, time series modeling is an effective technique. To improve results and make better decisions, time series models can be used in a variety of fields and applications.