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.
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.
R
# this line will download forecast package in your IDE install.packages ( 'forecast' ) library ( 'forecast' ) |
To check the kind of class the “AirPassengers” dataset belongs to using we can use the class method.
R
class (AirPassengers) |
Output:
"ts" // which means it is timeseries data.
We can also see the content of the dataset.
R
AirPassengers |
Output:
Plot the dataset to observe how the values have been changing from 1949 to 1960.
R
plot (AirPassengers) |
Output:
Time series data are decomposed into three components :
- Seasonal – Patterns that show how data is being changed over a certain period of time. Example – A clothing e-commerce website will have heavy traffic during festive seasons and less traffic during normal times. Here it is a seasonal pattern as value is being increased only at a certain period of time.
- Trend – It is a pattern that shows how values are being changed. For example how a website is running overall if running successfully trend goes up, if not, the trend comes down.
- Random – The remaining data of the time series after seasonal trends are removed is a random pattern. This is also known as noise.
R
data<- ts (AirPassengers, frequency=12) d<- decompose (data, "multiplicative" ) plot (d) |
Output:
The parameter multiplicative is added because time series data changes with the trend, if not so, such kinds of data are called “additive”.
Now we forecast 10 years of data by using Arima() function.
R
model<- auto.arima (AirPassengers) # h = 10*12 because, forecast is for 10 years for all 12 months f<- forecast (model, level= c (95), h=10*12) plot (f) |
Output:
The shaded region covers all the values that can possibly occur in the future10 years and the blue color pattern is the average of all values in the shaded part. This is how we can forecast values using any time series dataset.
ADVANTAGES AND DISADVANTAGES:
Advantages of using R for Time Series Forecasting:
- Large community: R has a large and active community of users and developers, which means that there are many resources and packages available for time series forecasting, and it also allows for easy collaboration and sharing of knowledge.
- Flexibility: R provides a wide range of tools and packages for time series forecasting, which allows for flexibility in selecting the appropriate method for a given dataset.
- Open-source: R is an open-source programming language, which means that it is free to use and can be modified to fit specific needs.
- Easy to use: R has a simple and intuitive syntax, which makes it easy to learn and use.
- High-quality visualization: R has powerful data visualization capabilities, which allows for easy interpretation and analysis of time series data.
Disadvantages of using R for Time Series Forecasting:
- Speed: R is an interpreted language, which can make it slower than compiled languages such as C or C++ for large datasets.
- Memory usage: R can be memory-intensive, which can be a problem for large datasets.
- Limited scalability: R is not designed for large-scale parallel computing, so it may not be suitable for large-scale time series forecasting tasks.
- Steep learning curve: R is a powerful programming language, but it has a steep learning curve, which can make it difficult for beginners.
- Lack of standardization: R provides a wide range of tools and packages for time series forecasting, which can lead to a lack of standardization in the way that time series forecasting tasks are performed, this could make it difficult to compare results across different studies.
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