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In today’s blog post, we shall look into time series analysis using R package – forecast. Objective of the post will be explaining the different methods available in forecast package which can be applied while dealing with time series analysis/forecasting.
A time series is a collection of observations of well-defined data items obtained through repeated measurements over time. For example, measuring the value of retail sales each month of the year would comprise a time series.
My data set contains data of Sales of CARS from Jan-2008 to Dec 2013.
MyData[1,1:14]
PART |
Jan08 |
FEB08 |
MAR08 |
.... |
.... |
NOV12 |
DEC12 |
MERC |
100 |
127 |
56 |
.... |
.... |
776 |
557 |
Table: shows the first row data from Jan 2008 to Dec 2012
Results:
The forecasts of the timeseries data will be:
Assuming that the data sources for the analysis are finalized and cleansing of the data is done, for further details,
As a first step, Understand the data visually, for this purpose, the data is converted to time series object using ts(), and plotted visually using plot() functions available in R.
ts = ts(t(data[,7:66]))
plot(ts[1,],type=’o’,col=’blue’)
Image above shows the monthly sales of an automobile
Forecast package is written by Rob J Hyndman and is available from CRAN here. The package contains Methods and tools for displaying and analyzing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Before going into more accurate Forecasting functions for Time series, let us do some basic forecasts using Meanf(), naïve(), random walk with drift – rwf() methods. Though these may not give us proper results but we can use the results as bench marks.
All these forecasting models returns objects which contain original series, point forecasts, forecasting methods used residuals. Below functions shows three methods & their plots.
Library(forecast)
mf = meanf(ts[,1],h=12,level=c(90,95),fan=FALSE,lambda=NULL)
plot(mf)
mn = naive(ts[,1],h=12,level=c(90,95),fan=FALSE,lambda=NULL)
plot(mn)
md = rwf(ts[,1],h=12,drift=T,level=c(90,95),fan=FALSE,lambda=NULL)
plot(md)
Once the model has been generated the accuracy of the model can tested using accuracy(). The Accuracy function returns MASE value which can be used to measure the accuracy of the model. The best model is chosen from the below results which gives have relatively lesser values of ME,RMSE,MAE,MPE,MAPE,MASE.
> accuracy(md)
ME RMSE MAE MPE MAPE MASE
Training set 1.806244e-16 2.445734 1.889687 -41.68388 79.67588 1.197689
accuracy(mf)
ME RMSE MAE MPE MAPE MASE
Training set 1.55489e-16 1.903214 1.577778 -45.03219 72.00485 1
> accuracy(mn)
ME RMSE MAE MPE MAPE MASE
Training set 0.1355932 2.44949 1.864407 -36.45951 76.98682 1.181666
A typical time-series analysis involves below steps:
A stationary time series is one whose properties do not depend on the time at which the series is observed. Time series with trends, or with seasonality, are not stationary.
The stationarity /non-stationarity of the data can be known by applying Unit Root Tests - augmented Dickey–Fuller test (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test.
ADF: The null-hypothesis for an ADF test is that the data are non-stationary. So large p-values are indicative of non-stationarity, and small p-values suggest stationarity. Using the usual 5% threshold, differencing is required if the p-value is greater than 0.05.
library(tseries)
adf = adf.test(ts[,1])
adf
Augmented Dickey-Fuller Test
data: ts[, 1]
Dickey-Fuller = -4.8228, Lag order = 3, p-value = 0.01
alternative hypothesis: stationary
The above figure suggests us that the data is of stationary and we can go ahead with ARIMA models.
KPSS: Another popular unit root test is the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. This reverses the hypotheses, so the null-hypothesis is that the data are stationary. In this case, small p-values (e.g., less than 0.05) suggest that differencing is required.
kpss = kpss.test(ts[,1])
Warning message:
In kpss.test(ts[, 1]) : p-value greater than printed p-value
kpss
KPSS Test for Level Stationarity
data: ts[, 1]
KPSS Level = 0.1399, Truncation lag parameter = 1, p-value = 0.1
Differencing:
Based on the unit test results we identify whether the data is stationary or not. If the data is stationary then we choose optimal ARIMA models and forecasts the future intervals. If the data is non- stationary, then we use Differencing - computing the differences between consecutive observations. Use ndiffs(),diff() functions to find the number of times differencing needed for the data & to difference the data respectively.
ndiffs(ts[,1])
[1] 1
diff_data = diff(ts[,1])
Time Series:
Start = 2
End = 60
Frequency = 1
[1] 1 5 -3 -1 -1 0 3 1 0 -4 4 -5 0 0 1 1 0 1 0 0 2 -5 3 -2 2 1 -3 0 3 0 2 -1 -5 3 -1
[36] -1 2 -1 -1 5 -2 0 2 -2 -4 0 3 1 -1 0 0 0 -2 2 -3 4 -3 2 5
Now retest for stationarity by applying acf()/kpss() functions if the plots shows us the Stationarity then Go ahead by applying ARIMA Models.
Identify Seasonality/Trend:
The seasonality in the data can be obtained by the stl()when plotted
Stl = Stl(ts[,1],s.window=”periodic”)
Series is not period or has less than two periods
Since my data doesn’t contain any seasonal behavior I will not touch the Seasonality part.
ARIMA Models:
For forecasting stationary time series data we need to choose an optimal ARIMA model (p,d,q). For this we can use auto.arima() function which can choose optimal (p,d,q) value and return us. Know more about ARIMA from here.
auto.arima(ts[,2])
Series: ts[, 2]
ARIMA(3,1,1) with drift
Coefficients:
ar1 ar2 ar3 ma1 drift
-0.2621 -0.1223 -0.2324 -0.7825 0.2806
s.e. 0.2264 0.2234 0.1798 0.2333 0.1316
sigma^2 estimated as 41.64: log likelihood=-190.85
AIC=393.7 AICc=395.31 BIC=406.16
Forecast time series:
Now we use forecast() method to forecast the future events.
forecast(auto.arima(dif_data)) Point Forecast Lo 80 Hi 80 Lo 95 Hi 9561 -3.076531531 -5.889584 -0.2634795 -7.378723 1.22566062 0.231773625 -2.924279 3.3878266 -4.594993 5.05854063 0.702386360 -2.453745 3.8585175 -4.124500 5.52927264 -0.419069906 -3.599551 2.7614107 -5.283195 4.44505565 0.025888991 -3.160496 3.2122736 -4.847266 4.89904466 0.098565814 -3.087825 3.2849562 -4.774598 4.97172967 -0.057038778 -3.243900 3.1298229 -4.930923 4.81684668 0.002733053 -3.184237 3.1897028 -4.871317 4.87678369 0.013817766 -3.173152 3.2007878 -4.860232 4.88786870 -0.007757195 -3.194736 3.1792219 -4.881821 4.866307
plot(forecast(auto.arima(dif_data)))
The below flow chart will give us a summary of the time series ARIMA models approach:
The above flow diagram explains the steps to be followed for a time series forecasting
Please visit my blog dataperspective for more articles
Comment
I like your post. However, it's hard to follow or replicate your method without a reference or access to the car sales data set. Am I missing something?
this post is a blatant copy from Rob J Hyndman's book .Atleast acknowledge the source
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