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Rebecca Merrett
tutorials
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a54313e9
Commit
a54313e9
authored
Apr 19, 2019
by
Rebecca Merrett
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r_time_series_example.R
Time Series/r_time_series_example.R
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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/
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