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.

####

Table: shows the first row data from Jan 2008 to Dec 2012

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,

ts = ts(t(data[,7:66]))

plot(ts[1,],type=’o’,col=’blue’)

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)

> 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

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.

library(tseries)

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.

Now retest for stationarity by applying acf()/kpss() functions if the plots shows us the Stationarity then Go ahead by applying ARIMA Models.

The seasonality in the data can be obtained by the stl()when plotted

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.

### What is Time Series?

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.#### Objective:

- Identify patterns in the data – stationarity/non-stationarity.
- Prediction from previous patterns.

### Time series Analysis in R:

My data set contains data of Sales of CARS from Jan-2008 to Dec 2013.####
*Problem Statement:* Forecast sales for 2013

*Problem Statement:*

**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:**

Assuming that the data sources for the analysis are finalized and cleansing of the data is done, for further details,

#### Step1: Understand the data:

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 & methods:

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)

#### Measuring accuracy:

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

#### Step2: Time Series Analysis Approach:

A typical time-series analysis involves below steps:- Check for identifying under lying patterns - Stationary & non-stationary, seasonality, trend.
- After the patterns have been identified, if needed apply Transformations to the data – based on Seasonality/trends appeared in the data.
- Apply forecast() the future values using Proper ARIMA model obtained by auto.arima() methods.

#### Identify Stationarity/Non-Stationarity:

**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.
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 95

`61 -3.076531531 -5.889584 -0.2634795 -7.378723 1.225660`

`62 0.231773625 -2.924279 3.3878266 -4.594993 5.058540`

`63 0.702386360 -2.453745 3.8585175 -4.124500 5.529272`

`64 -0.419069906 -3.599551 2.7614107 -5.283195 4.445055`

`65 0.025888991 -3.160496 3.2122736 -4.847266 4.899044`

`66 0.098565814 -3.087825 3.2849562 -4.774598 4.971729`

`67 -0.057038778 -3.243900 3.1298229 -4.930923 4.816846`

`68 0.002733053 -3.184237 3.1897028 -4.871317 4.876783`

`69 0.013817766 -3.173152 3.2007878 -4.860232 4.887868`

`70 -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

```
```

nice information on data science has given thank you very much.

ReplyDeleteData Science Training in Hyderabad

Great Article

DeleteData Mining Projects

Python Training in Chennai

Project Centers in Chennai

Python Training in Chennai

good article about data science has given it is very nice thank you for sharing.

ReplyDeleteData Science Training in Hyderabad

Hello, I have a question about "forecast" package (I am using it in Tableau charts). Is there a way to ignore/exclude current month from the calculations?

ReplyDeleteIndependent of the size of a venture, it in every case needs the influence that information examination (of the correct information obviously) can give. artificial intelligence course

ReplyDeleteThanks for a nice tutorial. Are the CARS data you used available for comparison of results with other methods? Is this the same as the standard "cars" dataset used throughout R? Or is it different?

ReplyDeletePlus, it will empower you with data management technologies like machine learning, Flume, and Hadoop. artificial intelligence certification

ReplyDeleteTra vé máy bay giá rẻ tại Aivivu, tham khảo

ReplyDeletevé máy bay đi Mỹ Vietnam Airline

vé máy bay từ seattle về việt nam

khi nào có chuyến bay từ canada về việt nam

Lịch bay từ Hàn Quốc về Việt Nam tháng 7

Thanks for posting the best information and the blog is very helpful.

ReplyDeleteartificial intelligence course in hyderabadInformative blog

ReplyDeleteai training in hyderabad

Don’t forget to always keep your customers in mind when settling on a box style for your lipstick boxes. For instance, if sustainability is a priority for your ideal customer, consider spending a little more to get boxes made from post-consumer waste.

ReplyDeleteI like your post. I appreciate your blogs because they are really good. Please go to this website for Data Science course in Bangalore. These courses are wonderful for professionals.

Great very helpful blog. Thanks For Sharing Such A Wonderful Blog. I will definitely go ahead and take advantage of this. Your Blog Is Very Informative. Again Thanks For Sharing This Blogs With Us. For more learning go through Skillslash.

ReplyDeleteFor Data Science Course Data Science Course In Bangalore

This post is very simple to read and appreciate without leaving any details out. Great work!

ReplyDelete360DigiTMG data analytics course