Skip to content
Projects
Groups
Snippets
Help
This project
Loading...
Sign in / Register
Toggle navigation
tutorials
Overview
Overview
Details
Activity
Cycle Analytics
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Charts
Issues
0
Issues
0
List
Board
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Charts
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Charts
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
Grant Vigors
tutorials
Commits
a54313e9
Commit
a54313e9
authored
Apr 19, 2019
by
Rebecca Merrett
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Upload New File
parent
7ea6d79b
Hide whitespace changes
Inline
Sidebyside
Showing
1 changed file
with
180 additions
and
0 deletions
+180
0
r_time_series_example.R
Time Series/r_time_series_example.R
+180
0
No files found.
Time Series/r_time_series_example.R
0 → 100644
View file @
a54313e9
# Install and load packages
#install.packages("forecast")
library
(
forecast
)
#install.packages("tseries")
library
(
tseries
)
# Set your working directory to where your script and
# data files sit on your local computer
setwd
(
"C:\\Users\\Rebecca\\Desktop\\time_series"
)
# Read csv file of train dataset as a univariate
# (single variable) series, with datetime
# (column 1) as the row index
hourly_sentiment_series
<
read.csv
(
file
=
"hourly_users_sentiment_subset.csv"
,
sep
=
","
,
row.names
=
1
,
header
=
TRUE
)
head
(
hourly_sentiment_series
)
# Check data is indexed with rows/index as the datetime values
rownames
(
hourly_sentiment_series
)
# Preview the data to get an idea of the values and sample size
head
(
hourly_sentiment_series
)
tail
(
hourly_sentiment_series
)
dim
(
hourly_sentiment_series
)
# Plot the data to check if stationary (constant mean and variance),
# as many time series models require the data to be stationary
plot
(
hourly_sentiment_series
$
users_sentiment_score
,
type
=
"l"
,
xlab
=
"Datetime"
,
ylab
=
"Users Sentiment Score"
)
# Difference the data to make it more stationary
# and plot to check if the data looks more stationary
# Differencing subtracts the next value by the current value
# Best not to overdifference the data,
# as this could lead to inaccurate estimates
# Make sure to leave no missing values, as this could cause
# problems when modeling later
hourly_sentiment_series_diff1
<
diff
(
hourly_sentiment_series
$
users_sentiment_score
)
plot
(
hourly_sentiment_series_diff1
,
type
=
"l"
,
xlab
=
"Datetime"
,
ylab
=
"Users Sentiment Score"
)
hourly_sentiment_series_diff2
=
diff
(
hourly_sentiment_series_diff1
)
plot
(
hourly_sentiment_series_diff2
,
type
=
"l"
,
xlab
=
"Datetime"
,
ylab
=
"Users Sentiment Score"
)
# Check ACF and PACF plots to determine number of AR terms and
# MA terms in ARMA model, or to spot seasonality/periodic trend
# Autoregressive forecast the next timestamp's value by
# regressing the previous values
# Moving Average forecast the next timestamp's value by
# averaging the previous values
# Autoregressive Integrated Moving Average is useful
# for nonstationary data, plus has an additional seasonal
# differencing parameter for seasonal nonstationary data
# ACF and PACF plots include 95% Confidence Interval bands
# Anything outside of the CI shaded bands is a
# statistically significant correlation
# If we see a significant spike at lag x in the ACF
# that helps determine the number of MA terms
# If we see a significant spike at lag x in the PACF
# that helps us determine the number of AR terms
acf
(
hourly_sentiment_series_diff2
)
pacf
(
hourly_sentiment_series_diff2
)
# Depending on ACF and PACF, create ARMA/ARIMA model
# with AR and MA terms
# auto.arima will automatically choose best terms
ARMA1model_hourly_sentiment
<
auto.arima
(
hourly_sentiment_series
,
d
=
2
)
# If the pvalue for a AR/MA coef is > 0.05, it's not significant
# enough to keep in the model
# Might want to remodel using only significant terms
ARMA1model_hourly_sentiment
# Predict the next 5 hours (5 time steps ahead),
# which is the test/holdout set
ARMA1predict_5hourly_sentiment
<
predict
(
ARMA1model_hourly_sentiment
,
n.ahead
=
5
)
ARMA1predict_5hourly_sentiment
# Back transform so we can compare dediff'd predicted values
# with the dediff'd/original actual values
# This is automatically done when forecasting, so no need to
# manually dediff
# Nevertheless, let's demo how we detransform 2 rounds of diffs
# using cumulative sum with original data given
#diff2 back to diff1
undiff1
<
cumsum
(
c
(
hourly_sentiment_series_diff1
[
1
],
hourly_sentiment_series_diff2
))
all
(
round
(
hourly_sentiment_series_diff1
)
==
round
(
undiff1
))
#undiff1 back to original data
undiff2
<
cumsum
(
c
(
hourly_sentiment_series
$
users_sentiment_score
[
1
],
undiff1
))
all
(
round
(
undiff2
,
6
)
==
round
(
hourly_sentiment_series
,
6
))
#Note: very small differences
head
(
hourly_sentiment_series
$
users_sentiment_score
)
head
(
undiff2
)
# Plot actual vs predicted
# First let's get 2 versions of the time series:
# All values with the last 5 being actual values
# All values with last 5 being predicted values
hourly_sentiment_full_actual
<
read.csv
(
file
=
"hourly_users_sentiment_sample.csv"
,
sep
=
","
,
row.names
=
1
,
header
=
TRUE
)
tail
(
hourly_sentiment_full_actual
)
indx_row_values
<
row.names
(
hourly_sentiment_full_actual
)[
20
:
24
]
indx_row_values
ARMA1predict_5hourly_sentiment
[[
1
]]
predicted_df
<
data.frame
(
indx_row_values
,
ARMA1predict_5hourly_sentiment
[[
1
]])
hourly_sentiment_series_df
<
read.csv
(
file
=
"hourly_users_sentiment_subset.csv"
,
sep
=
","
,
header
=
TRUE
)
predicted_df
<
setNames
(
predicted_df
,
names
(
hourly_sentiment_series_df
))
hourly_sentiment_full_predicted
<
rbind
(
hourly_sentiment_series_df
,
predicted_df
)
hourly_sentiment_full_predicted
<
data.frame
(
hourly_sentiment_full_predicted
,
row.names
=
1
)
tail
(
hourly_sentiment_full_predicted
)
# Now let's plot actual vs predicted
plot
(
hourly_sentiment_full_predicted
$
users_sentiment_score
,
type
=
"l"
,
col
=
"orange"
,
xlab
=
"Datetime"
,
ylab
=
"Users Sentiment Score"
)
lines
(
hourly_sentiment_full_actual
$
users_sentiment_score
,
type
=
"l"
,
col
=
"blue"
)
legend
(
"topleft"
,
legend
=
c
(
"predicted"
,
"actual"
),
col
=
c
(
"orange"
,
"blue"
),
lty
=
1
,
cex
=
0.5
)
# Calculate the MAE to evaluate the model and see if there's
# a big difference between actual and predicted values
actual_values_holdout
<
hourly_sentiment_full_actual
$
users_sentiment_score
[
20
:
24
]
predicted_values_holdout
<
hourly_sentiment_full_predicted
$
users_sentiment_score
[
20
:
24
]
prediction_errors
<
c
()
for
(
i
in
1
:
(
length
(
actual_values_holdout
))){
err
<
actual_values_holdout
[
i
]

predicted_values_holdout
[
i
]
prediction_errors
<
append
(
prediction_errors
,
err
)
}
prediction_errors
mean_absolute_error
<
mean
(
abs
(
prediction_errors
))
mean_absolute_error
# You could also look at RMSE
# Would you accept this model as it is?
# There are a few problems to be aware of:
# Data might not be stationary  even though looked
# fairly stationary to our judgement, a test would
# help better determine this
# Test (using DickeyFuller test) to check if 2 rounds
# of differencing resulted in stationary data or not
# Print pvalue:
# If > 0.05 accept the null hypothesis, as the data
# is nonstationary
# If <= 0.05 reject the null hypothesis, as the data
# is stationary
adf.test
(
hourly_sentiment_series_diff2
)
#Need to better transform these data:
# You could look at stabilizing the variance by applying
# the cube root for neg and pos values and then
# difference the data
#You might compare models with different AR and MA terms
#This is a very small sample size of 24 timestamps,
# so might not have enough to spare for a holdout set
# To get more use out of your data for training, rolling over time
# series or timestamps at a time for different holdout sets
# allows for training on more timestamps; doesn't stop the model from
# capturing the last chunk of timestamps stored in a single holdout set
#The data only looks at 24 hours in one day
# Would we start to capture more of a trend in hourly sentiment if we
# collected data over several days?
# How would you go about collecting more data?
# Take on the challenge and further improve this model:
# You have been given a head start, now take this example
# and improve on it!
# To study time series further:
#Look at model diagnostics
#Use AIC to search best model parameters
#Handle any datetime data issues
#Try other modeling techniques
# Learn more during a short, intense bootcamp:
# Time Series to be introduced in Data Science Dojo's
# post bootcamp material
# Data Science Dojo's bootcamp also covers some other key
# machine learning algorithms and techniques and takes you through
# the critical thinking process behind many data science tasks
# Check out the curriculum: https://datasciencedojo.com/bootcamp/curriculum/
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment