The Complete Guide to Time Series Models
Tags: data, analytics, algorithms
Published: 2023-10-19 12:22:12

> ##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.

![Example of a stationary process](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/viqgs21fcnpyyw419qiz.PNG)
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.


![Example of seasonality](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/7h7exskh2mkvdxkab3fj.PNG)

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

![Image of trend ](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qayk1vxwwphzbavc5tyl.PNG)

##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.