From f27b50e7771b822827d17e29da49f65a5fec1d65 Mon Sep 17 00:00:00 2001
From: Rebecca Merrett
Date: Tue, 7 May 2019 23:09:33 +0000
Subject: [PATCH] Update r_time_series_example.R

Time Series/r_time_series_example.R  66 +++++++++++++++++++++++++++++++++
1 file changed, 33 insertions(+), 33 deletions()
diff git a/Time Series/r_time_series_example.R b/Time Series/r_time_series_example.R
index 0bf9506..37dd7a9 100644
 a/Time Series/r_time_series_example.R
+++ b/Time Series/r_time_series_example.R
@@ 145,36 +145,36 @@ mean_absolute_error
# 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/"
+#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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