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Chapter 1 There are so many data sets available which involve time components and these type of prediction were neglected often because of the time formats and made it very difficult to handle

Chapter 1 There are so many data sets available which involve time components and these type of prediction were neglected often because of the time formats and made it very difficult to handle. Thus time series forecasting became important area of machine learning. A time series is a set of chronologically arranged data points collected over time. The data points are recorded in the order of their occurrence. Time series analysis is very important in forecasting the future behavior and analyzing the past behavior of the datasets and comparing datasets with other time series as well. It is particularly important while dealing with yearly Government budgets, monthly sales and advertising expenditures, and temperature distributions. Most likely, you will be working with data that already exists or the data that has been imported from other sources. Often, such data is not in your preferred class or format. You will be struggling with data which is not in the frame that you need. The R software helps convert data into the preferred type of data. It provides you several packages to support your forecasting analysis and build forecasting models in time series such as ARIMA, GARCH, and EGARCH. This chapter discusses the general time series data and the other types of data that are converted to time series data, followed by the time series models. In this chapter, you will learn to Use time series data Convert any data to time series data in R Forecast time series models in R Time series are used in many domains including weather forecasting, finance, and earthquake prediction. The datasets and the models that were discussed before in the Machine Learning with R – Student Guide arise in the situations where observations are independent of each other but in case of time series data sets observations are dependent. Here we have to deal with the change in frequencies with respect to time. The given example of time.series data explains a time series in which the observations are dependent on the previously observed data. In this example, it is necessary to develop time series models. Since the time series is sequential in nature, the time stamp is displayed in years and the average speed in km/hour. time.series – read.csv(../time_series.csv, header FALSE) Average_speed – ts(time.seriesV5, start 2010, frequency 12) class(Average_speed) plot.ts(Average_speed) Figure 1 Average speed with respect to time The time stamp may be displayed in nanoseconds, seconds, minutes, hours, days, or months. Any case of dependent variable or a variable of interest which we observe on regular basis is treated as time series. For example, the data related to economy, businesses, GDP, and unemployment, which is used to make future predictions, is stored in the form of time series. Thus, time series plays a crucial role in various domains. An observed time series can be decomposed into systematic and non-systematic components. Systematic components These components of time series are consistent and can be described and modeled directly. Trend (Secular trend or Long term variation)It is a long pattern that produces irregular effects. It can be positive, negative, linear, or nonlinear. It represents the variations of low frequency. The high and medium frequency of data is filtered out from the time series. For example, if we have 10 years data of weather reports for a particular country, then long term trend appears and if we have data for almost 100 years of weather reports for the same country, then very long term variation will appear. Cyclic (Cyclic variation) It is a pattern that follows a specific trend of up and down movement. The duration of cyclic variations depends on business problems which needs to be analyzed using time series. Seasonal (Seasonal variation) It is a pattern that reflects regular fluctuations which are related to periodic cycles. Many time series data indicate seasonal fluctuations in monthly and quarterly cycles of the year. Non-systematic components These components of time series cannot be modeled directly. Residuals (irregular variation) These are the residuals of the original time series after the trend and cyclic variations are removed. They are unpredictable like accidents and earthquakes. They are also referred as noise or errors which can be removed by time series techniques. There are some techniques available for stationary time series as well which assume the mean and variance to be static as required by future prediction analysis. If the time series is non-stationary, it is first converted into stationary time series and then further statistical analysis is done. R programming software facilitate us with many default functions and packages, which convert can convert any type of data into time series data. We store that data into R is defined as time series object. We can use timeseries object functions to create, modify, and analyze the behavior of a time series. Function ts() creates the time series objects in R. The basic syntax for the ts() function in time series analysis is Timeseries.data- ts(data, start, end, frequency) Here, data is a matrix or vector containing the values used in time series. start indicates the start time for the first observation in time series. end specifies the end time for the last observation in time series. frequency specifies the number of observations per unit time. Consider an example of a vector representing the temperature for plotting a time series graph. Temperature – c(15,34,56,34,67,34.5,98,56,78,23,10,23) Temperature Convert this data set into time series object using following code. temp.timeseries – ts(Temperature,start c(2010,1),frequency 12) temp.timeseries This is the output produced by the code. JanFebMarAprMayJunJulyAugSepOctNovDec2010 15.034.056.034.067.034.598.056.078.023.010.023.0 Next, plot this data by using the following code. plot(temp.timeseries) Figure 2 Time series plot using ts() function You can also plot multiple datasets of time series in one plot by combining multiple series into a matrix. Consider two datasets in the form of vector in R for creating multiple time series plot. Vector1 – c(15,34,56,34,67,34.5,98,56,78,23,10,23) Vector1 Vector2 – c(25,56,78,65,90,100,132,200,213,78,65,34) Vector2 Next, convert the datasets into to a matrix. combined.vector – matrix(c(vector1, vector2),nrow 12) Next, convert this data set into a time series object. vector.timeseries – ts(combined.vector,start c(2012,1),frequency 12) vector.timeseries The code produces the following output. Table 1 Table showing multiple time series objects Next, plot this data by using the following code. plot(vector.timeseries, main Multiple Time Series) This code will generate the following plot. Figure 3 Multiple time series plot using ts() function Frequency parameter in ts() function defines the number of observations per unit time or the time interval between the data points. Frequency 4, specifies the data at quarterly basis Frequency 5, specifies the data at daily basis (business days) Frequency 6, specifies the data at every 10 minutes of an hour Frequency 12, specifies the data at monthly basis The only constant parameter in time series is data. There are many types of data are collected over the time like quality measurements, stock prices, weather reports, population growth etc. For this time based data needs to be forecasted by time series models. Time series use those models for forecasting which are based on previously occupied values in your data. This section discusses about the given topics time series functions Construction of time series objects Application of time series models The zoo package provides a set of powerful tools to manage ordered observations. It aims at performing calculations containing irregular time series of numeric matrices and factors. Zoo also supports weekly regular series which have an underlying regularity. The time series object ts() has certain limitations with unequally spaced data. It cant be used to represent the daily level stock prices because the stock prices are equally spaced for Monday to Friday, but not for Friday to Monday or in case of market holidays on weekdays. On the other hand, zoo is flexible and performs easily on unequally spaced data and numerically indexed data. You can install and load the zoo library by executing the following code. install.packages(zoo) library(zoo) Let convert the given list into date format using as.Date() A.Date – as.Date(paste(2003, 02, c(1, 3, 7, 9, 14), sep -)) Note as.Date()converts character data to calendar dates. The format isas.Date(x,format), wherexrepresents the character data andformatrepresents the desired format. Univariate plotting using zoo Univariate time series plot is the simplest form of plot. It represent the single line of observation recorded over regular observations in a single plot such as monthly returns etc. Here we are creating a univariate plot using zoo()function for randomly distributed numbers and a vector which we have created above i.e. A.Date. A – zoo(rnorm(5), A.Date) A2 – zoo(rnorm(5, sd 0.2), A.Date) plot(A) lines(A2, col 2) Figure 4 Univariate plotting using zoo Multivariate plotting using zoo Multivariate time series plot is a form of plot with single panel having first line of plot as the main curve line and remaining curves as functional lines. It represent the multiple lines in a single plot. Here we are creating a multivariate plot using zoo()function for randomly distributed numbers and a vector which we have created above i.e. A.Date. z – cbind(A, A2, zoo(rnorm(5, sd 0.5), A.Date)) colnames(z) – LETTERS13 plot(z, plot.type single, col list(B 2)) plot(z, type b, pch 13, col 13) plot(z, type b, pch list(A 15, B 3), col list(C 4, 2)) plot(z, type b, screen c(1,2,1), col 13) Figure 5 Multivariate plotting using zoo Barplot using zoo We can also create a bar plot using zoo object. Bar plot basically represent the numerical data with rectangular bars proportional to the value they belong. zoo() function. A – zoo(cbind(rpois(5, 2), rpois(5, 3)), A.Date) barplot(A, beside TRUE) Figure 6 Barplot using zoo xts is also an another type of time series object which provides uniform handling of time based data by extending zoo. It boost the endemic format of information and allows user level custom make changes and extension. It carries all the features of a zoo object. It consists of a matrix and index which has to be time-based. The xts objects can be thought of as a matrix of observations combined with an index of corresponding dates and times. Construction of an xts object install.packages(xts) library(xts) Create the object data using 5 random numbers. data – rnorm(5) Create dates as a Date class object starting from 2017-01-01. dates – seq(as.Date(2017-01-01), length 5, by days) Use xts() to create data1. data1 – xts(x data, order.by dates) Create data2 (1899-05-08) using a POSIXct date class object. data2 – as.POSIXct(1899-05-08) Create data3 and add a new attribute called born. data3 – xts(x data, order.by dates, born data2) Output Table 2 Output table of xts object In the above example, we have created an xts object or a time series object for further analysis. This xts objects results as a matrix with index of corresponding dates and times. Note as.POSIXct() function convert an object data into date or time format. The first step in time series analysis is to extract trend, seasonal, and cyclical components. Linear filters is one of the methods that is used to apply filters to extract trend from the time series. The first step in time series analysis is to extract trend, seasonal, and cyclical components. Linear filters is one of the methods that is used to apply filters to extract trend from the time series. The function used for finding filters is filter(x,filter) where x is the time series data and filter is the coefficient required. Lets take an example time_series data in time series and plot the weekly and monthly averages. The function used for finding filters is filter(x,filter) where x is the time series data and filter is the coefficient required to find the average speed. Now we will take an example of one of the variable of time_series data set, we will convert variable V8 i.e. AvgMovingSpeed of time_series data set into time series variable and name it Average_speed. Then we will plot the weekly and monthly averages. time.series – read.csv(C/Users/Amita.9.Jain/Desktop/DA/BOOK/time series code/time_series.csv, header FALSE) time.series Average_speed – ts(time.seriesV8, start 2010, frequency 12) plot(Average_speed, typel) Average_speed.weekly – filter(Average_speed,filterrep(1/2,2)) Average_speed.monthly – filter(Average_speed,filterrep(1/10,10)) lines(Average_speed.weekly,colpurple) lines(Average_speed.monthly,colgreen) Figure 7 Average speed of time_series data using linear filter (NIIT Time series modeling 1. PAGE MERGEFORMAT 10 (NIIT Overview of SQL Server 1. PAGE MERGEFORMAT 3 (NIIT Time series modeling 1. PAGE MERGEFORMAT 11 Objectives Exploring Time Series Note General Time Series Conversion of Any Data to Time Series Data in R Multiple Time Series Different Time Intervals Frequency Note Forecasting Time Series Models in R Constructing zoo Objects Constructing xts objects Linear Filters [email protected] /3K/IS3/_
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