What is Time Series Forecasting?
Time series forecasting focuses on making predictions about future events or values using past and present data points. Data points are gathered over time, and this technique is used in a variety of fields, such as sales, finance, weather forecasting, and economics. The following are some important ideas and methods to consider when carrying out time series forecasting.
To apply any models of time series forecasting we need to make it stationary. Stationary means time series should have constant mean, constant variance and constant autocorrelation. We need to remove seasonality and trends from the data. Seasonality can be additive or multiplicative. We use different transformation on data to remove trends and seasonality. We can test the time series stationary or not by using Dickey-Fuller test.
There are multiple methods and tricks present to make the time series stationary. We need to remove seasonal elements and trends before making any forecast. Most of the real-world time series data contains both seasonality and trends. Time series forecasting model does not need these properties for better forecasting. To detrend the time series we can take moving average. This can be done by window functions . We can also use linear regression and fit a line along with the data. Removing seasonality is difficult and it needs a lot of domain knowledge. For example, in a drug sale, seasonality may exist due to winter in each year. We can use locally weighted scatterplot smoothing in some cases to remove seasonality.
Time Series Data: Time series data consists of observations or measurements collected at regular time intervals. These data points are typically plotted over time, and the goal of time series forecasting is to predict future values in this sequence.
Components of Time Series:
- Trend: The long-term movement or direction in the data. Trends can be upward (increasing), downward (decreasing), or flat (constant).
- Seasonality: Repeating patterns or fluctuations that occur at fixed intervals. For example, sales of winter clothing may exhibit a yearly seasonality pattern.
- Cyclic Patterns: Longer-term, non-seasonal patterns that may not have fixed intervals. Cyclic patterns represent oscillations in the data that are not tied to a specific season.
- Irregularity (Noise): Random, unpredictable fluctuations in the data.
Time Series Forecasting Methods
Time series forecasting methods are techniques used to make predictions about future values in a time series based on historical and current data. There are several well-established methods for time series forecasting, each with its own strengths and weaknesses. Here are some of the most commonly used time series forecasting methods.
- Autoregressive Integrated Moving Average (ARIMA)
- Seasonal Decomposition of Time Series (STL)
- Seasonal Autoregressive Integrated Moving-Average (SARIMA)
There are so many more methods are available but these are the most common methods for time series forecasting.
Packages for Time Series Forcasting in R
In R Programming Language There are several R packages available for time series forecasting, including.
- “forecast”: This package provides a wide range of methods for time series forecasting, including exponential smoothing, ARIMA, and neural networks.
- “tseries”: This package provides functions for time series analysis and forecasting, including functions for decomposing time series data, and for fitting and forecasting models such as ARIMA.
- “prophet”: This package is developed by Facebook, it provides a simple and fast way to perform time series forecasting using additive models. It is designed for business time-series data and it is easy to interpret and to use.
- “rugarch”: This package provides a flexible and powerful framework for fitting and forecasting volatility models, including GARCH and its variants.
- “stlplus”: This package provides functions for decomposing time series data using the STL algorithm, which is useful for removing seasonal and trend components from time series data.
To use these packages, first, they need to be installed and loaded into R. Then, the time series data must be prepared and cleaned, and the appropriate forecasting method can be applied. The forecasted values can then be plotted, evaluated and compared to the actual values.
Forecasting is nothing but a prediction. Analyzing time-series data, observing hidden patterns in it, and predicting future trends using the previous ones is called forecasting. Some of the cool real-world applications of time series forecasting are as follows:
Application | Timeseries data | Forecasting Results |
|---|---|---|
| Weather prediction | The temperature of a place collected for one month | Forecast weather for the next few months in that place |
| Stock market price prediction | Stock market price data for one day and patterns | Predict the stock market price for the next day |
| E-commerce and Retail | Sales data of a company for one year | Predict revenue and number of sales for next year |
| Industrial management | The raw material used and available in 3-5 years | Raw materials requirements prediction, profit prediction |
Here we will use the AutoRegressive Integrated Moving Average which is nothing but the ARIMA method for forecasting using time series data. We will use AirPassengers(this dataset contains US airline passengers from 1949 to 1960) and forecast passenger data for 10 years that is from 1960-1970.
# this line will download forecast package in your IDE
install.packages('forecast')
library('forecast')
Time Series and Forecasting Using R
Time series forecasting is the process of using historical data to make predictions about future events. It is commonly used in fields such as finance, economics, and weather forecasting. R is a powerful programming language and software environment for statistical computing and graphics that is widely used for time series forecasting.