1. The document discusses time series analysis and visualization techniques using an electricity consumption dataset from Germany. 2. Key steps include cleaning the data, setting the date as the index, adding relevant columns, and visualizing consumption trends over various time periods using line and box plots. 3. The data is also resampled to the weekly level to analyze aggregate consumption patterns over longer time intervals.

1. AD3301
DATA EXPLORATION AND VISUALIZATION
Unit 5
TIME SERIES ANALYSIS
Fundamentals of TSA – Characteristics of time
series data – Data Cleaning – Time-based
indexing – Visualizing – Grouping –
Resampling.

2. Time series data
Time series data includes timestamps and is generated
while monitoring the industrial process or tracking any
business metrics.
An ordered sequence of timestamp values at equally
spaced intervals is referred to as a time series.
spaced intervals is referred to as a time series.
Analysis of a time series is used in many applications such
as sales forecasting, utility studies, budget analysis, economic
forecasting, inventory studies.
There many methods that can be used to model and
forecast time series.

3. Fundamentals of TSA
1. We can generate the dataset using the
numpy library:
import os import numpy as np
import os import numpy as np
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
zero_mean_series = np.random.normal(loc=0.0, scale=1.,
size=50)
print(zero_mean_series)

4. The output of the preceding code is given here:
[ 0.91315139 0.51955858 -1.03172053 -0.725203 1.88933611 -0.39631515
0.71957305 0.01773119 -1.88369523 0.62272576 -1.22417583 -0.3920638
0.45239854 0.15720562 0.11885262 -0.96940705 -1.20997492 0.93202519
-0.37246211 1.11134324 0.15633954 -0.5439416 0.16875613 0.2826228
0.58295158 0.3245175 0.42985676 0.97500729 0.24721019 -0.45684401
-0.58347696 -0.68752098 0.82822652 -0.72181389 0.39490961 -1.792727
-0.6237392 -0.24644562 -0.22952135 3.06311553 -3.05745406 1.37894995
-0.6237392 -0.24644562 -0.22952135 3.06311553 -3.05745406 1.37894995
-0.39553 -0.26359025 -0.21658428 0.63820235 -1.7740917 0.66671788
-0.89029947 0.39759542]

5. 2. Next, we use the seaborn library to plot the
time series data.
plt.figure(figsize=(16, 8))
g = sns.lineplot(data=zero_mean_series)
g.set_title('Zero mean model')
g.set_xlabel('Time index')
g.set_xlabel('Time index')
plt.show()

6. We plotted the time series graph using the seaborn.lineplot()
function which is a built-in method provided by the seaborn
library. The output of the preceding code is given here:

7. 3. We can perform a cumulative sum over the list and
then plot the data using a time series plot. The plot
gives more interesting results
random_walk = np.cumsum(zero_mean_series)
print(random_walk)
It generates an array of the cumulative sum as shown here:
[ 0.91315139 1.43270997 0.40098944 -0.32421356 1.56512255 1.1688074
1.88838045 1.90611164 0.0224164 0.64514216 -0.57903366 -0.97109746
1.88838045 1.90611164 0.0224164 0.64514216 -0.57903366 -0.97109746
-0.51869892 -0.36149331 -0.24264069 -1.21204774 -2.42202265 -1.48999747
-1.86245958 -0.75111634 -0.5947768 -1.1387184 -0.96996227 -0.68733947
-0.10438789 0.22012962 0.64998637 1.62499367 1.87220386 1.41535986
0.8318829 0.14436192 0.97258843 0.25077455 0.64568416 -1.14704284
-1.77078204 -2.01722767 -2.24674902 0.81636651 -2.24108755 -0.86213759
-1.25766759 -1.52125784 -1.73784212 -1.09963977 -2.87373147 -2.20701359
-3.09731306 -2.69971764]
Note that for any particular value, the next value is the sum of previous values.

8. 4. Now, if we plot the list using the time series plot as
shown here, we get an interesting graph that shows
the change in values over time:
plt.figure(figsize=(16, 8))
g = sns.lineplot(data=random_walk)
g.set_title('Random Walk')
g.set_title('Random Walk')
g.set_xlabel('Time index')
plt.show()

9. The output of the preceding code is given here:
Note the graph shown in the preceding diagram. It shows the
change of values over time.

10. Univariate time series
• When we capture a sequence of observations
for the same variable over a particular
duration of time, the series is referred to as
univariate time series.
• In general, in a univariate time series, the
• In general, in a univariate time series, the
observations are taken over regular time
periods.
• (E.g.) The change in temperature over time
throughout a day.

11. Characteristics of time series data
• Trend: When looking at time series data, it is essential to see if there
is any trend. Observing a trend means that the average measurement
values seem either to decrease or increase over time.
• Outliers: Time series data may contain a notable amount of outliers.
These outliers can be noted when plotted on a graph.
• Seasonality: Some data in time series tends to repeat over a certain
interval in some patterns. We refer to such repeating patterns as
interval in some patterns. We refer to such repeating patterns as
seasonality.
• Abrupt changes: Sometimes, there is an uneven change in time series
data. We refer to such uneven changes as abrupt changes. Observing
abrupt changes in time series is essential as it reveals essential
underlying phenomena.
• Constant variance over time: It is essential to look at the time series
data and see whether or not the data exhibits constant variance over
time.

12. Time Series Analysis (TSA) with Open
Power System Data
• We can use the Open Power System dataset to
discover how electricity consumption and
production varies over time in Germany.
• Importing the dataset
• Importing the dataset
# load time series dataset
df_power =
pd.read_csv("https://raw.githubusercontent.com/je
nfly/opsd/master/opsd_germany_daily.csv")
print(df_power.columns)

13. The output of the preceding code is given here:
Index(['Consumption', 'Wind', 'Solar', 'Wind+Solar'],
dtype='object')
The columns of the dataframe are described here:
• Date: The date is in the format yyyy-mm-dd.
• Consumption: This indicates electricity consumption in
GWh.
• Solar: This indicates solar power production in GWh.
• Wind+Solar: This represents the sum of solar and wind
power production in GWh.

14. Data cleaning
1. We can start by checking the shape of the dataset:
df_power.shape
The output of the preceding code is given here:
(4383, 5)
The dataframe contains 4,283 rows and 5 columns.
The dataframe contains 4,283 rows and 5 columns.
2. We can also check few entries inside the dataframe.
Let's examine the last 10 entries:
print(df_power.tail(10))

15. The output of the preceding code is given here:

16. 3. Next, let's review the data types of
each column in our df_power dataframe:
print(df_power.dtypes)
The output of the preceding code is given here:
Date object
Consumption float64
Consumption float64
Wind float64
Solar float64
Wind+Solar float64
dtype: object

17. 4. Note that the Date column has a data type of object. This is not
correct. So, the next step is to correct the Date column, as shown
here:
#convert object to datetime format
df_power['Date'] = pd.to_datetime(df_power['Date'])
5. It should convert the Date column to Datetime format. We can verify
this again:
print(df_power.dtypes)
The output of the preceding code is given here:
The output of the preceding code is given here:
Date datetime64[ns]
Consumption float64
Wind float64
Solar float64
Wind+Solar float64
dtype: object
Note that the Date column has been changed into the correct data
type.

18. 6. Let's next change the index of our dataframe
to the Date column:
df_power = df_power.set_index('Date')
df_power.tail(3)
The output of the preceding code is given
here:
here:
Note from the preceding screenshot that the Date column has
been set as DatetimeIndex

19. 7. We can simply verify this by using the code snippet given here:
Print(df_power.index)
The output of the preceding code is given here:
DatetimeIndex(['2006-01-01', '2006-01-02', '2006-01-03',
'2006-01-04', '2006-01-05', '2006-01-06', '2006-01-07',
'2006-01-08', '2006-01-09', '2006-01-10', ... '2017-12-22',
'2017-12-23', '2017-12-24', '2017-12-25', '2017-12-26',
'2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30',
'2017-12-31'],dtype='datetime64[ns]', name='Date', length=4383,
freq=None)
freq=None)
8. Since our index is the DatetimeIndex object, now we can use it to
analyze thedataframe. Let's add more columns to our dataframe to
make it easier. Let's add Year, Month, and Weekday Name:
# Add columns with year, month, and weekday name
df_power['Year'] = df_power.index.year
df_power['Month'] = df_power.index.month
df_power['Weekday Name'] = df_power.index.day_name()

20. 9. Let's display five random rows from the dataframe:
# Display a random sampling of 5 rows
print(df_power.sample(5, random_state=0))
The output of this code is given here:
Note that we added three more columns—Year, Month, and
Weekday Name. Adding these columns helps to make the
analysis of data easier.

21. Time-based indexing
Time-based indexing is a very powerful method of the pandas
library. Having time-based indexing allows using a formatted string
to select data.
See the following code, for example:
print(df_power.loc['2015-10-02'])
The output of the preceding code is given here:
Consumption 1391.05
Wind 81.229
Solar 160.641
Solar 160.641
Wind+Solar 241.87
Year 2015
Month 10
Weekday Name Friday
Name: 2015-10-02 00:00:00, dtype: object
Note that we used the pandas dataframe loc accessor. In the preceding
example, we used a date as a string to select a row. We can use all sorts of
techniques to access rows just as we can do with a normal dataframe
index.

22. Visualizing time series
Let's visualize the time series dataset. We will continue using the
same df_power dataframe:
1. The first step is to import the seaborn and matplotlib libraries:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize':(11, 4)})
plt.rcParams['figure.figsize'] = (8,5)
plt.rcParams['figure.dpi'] = 150
plt.rcParams['figure.dpi'] = 150
2. Next, let's generate a line plot of the full time series of Germany's
daily electricity consumption:
df_power['Consumption'].plot(linewidth=0.5)

23. The output of the preceding code is given here:
As depicted in the preceding screenshot, the y-axis shows the
electricity consumption and the x-axis shows the year.
However, there are too many datasets to cover all the years.

24. 3. Let's use the dots to plot the data for all the other columns:
cols_to_plot = ['Consumption', 'Solar', 'Wind']
axes = df_power[cols_to_plot].plot(marker='.', alpha=0.5,
linestyle='None',figsize=(14, 6), subplots=True)
for ax in axes:
ax.set_ylabel('Daily Totals (GWh)')
The output of the preceding code is given here:
The output shows that electricity consumption can be broken down into two
distinct patterns:
One cluster roughly from 1,400 GWh and above
Another cluster roughly below 1,400 GWh
Moreover, solar production is higher in summer and lower in winter. Over the years,
there seems to have been a strong increasing trend in the output of wind power.

25. 4. We can further investigate a single year to have a closer look.
Check the code given here:
ax = df_power.loc['2016', 'Consumption'].plot()
ax.set_ylabel('Daily Consumption (GWh)');
The output of the preceding code is given here:
From the preceding screenshot, we can see clearly the
consumption of electricity for 2016.
The graph shows a drastic decrease in the consumption of
electricity at the end of the year(December) and during August.

26. Let's examine the month of December 2016 with the following
code block:
ax = df_power.loc['2016-12',
'Consumption'].plot(marker='o', linestyle='-')
ax.set_ylabel('Daily Consumption (GWh)');
The output of the preceding code is given here:
As shown in the preceding graph, electricity consumption is higher
on weekdays and lowest at the weekends. We can see the
consumption for each day of the month. We can zoom in further to
see how consumption plays out in the last week of December.

27. In order to indicate a particular week of December, we can supply a specific date
range as shown here:
ax = df_power.loc['2016-12-23':'2016-12-30',
'Consumption'].plot(marker='o', linestyle='-')
ax.set_ylabel('Daily Consumption (GWh)');
As illustrated in the preceding code, we want to see the electricity consumption
between 2016-12-23 and 2016-12-30. The output of the preceding code is given here:
As illustrated in the preceding screenshot, electricity consumption was lowest
on the day of Christmas, probably because people were busy partying. After
Christmas, the consumption increased.

28. Grouping time series data
1. We can first group the data by months and then use the
box plots to visualize the data:
fig, axes = plt.subplots(3, 1, figsize=(8, 7), sharex=True)
for name, ax in zip(['Consumption', 'Solar', 'Wind'], axes):
sns.boxplot(data=df_power, x='Month', y=name, ax=ax)
ax.set_ylabel('GWh')
ax.set_title(name)
if ax != axes[-1]:
ax.set_xlabel('')
The output of the preceding code is given here:
The output of the preceding code is given here:

29. 2. Next, we can group the consumption of electricity by the
day of the week, and present it in a box plot:
sns.boxplot(data=df_power, x='Weekday Name',
y='Consumption');
The output of the preceding code is given here:
The preceding screenshot shows that electricity consumption is higher on
weekdays than on weekends. Interestingly, there are more outliers on the
weekdays.

30. Resampling time series data
It is often required to resample the dataset at lower or higher frequencies. This
resampling is done based on aggregation or grouping operations. For example, we can
resample the data based on the weekly mean time series as follows:
1. We can use the code given here to resample our data:
columns = ['Consumption', 'Wind', 'Solar', 'Wind+Solar']
power_weekly_mean = df_power[columns].resample('W').mean()
power_weekly_mean
The output of the preceding code is given here:
As shown in the preceding screenshot, the first row, labeled 2006-01-01, includes the
average of all the data. We can plot the daily and weekly time series to compare the
dataset over the six-month period.

31. 2. Let's see the last six months of 2016. Let's start by initializing
the variable:
start, end = '2016-01', '2016-06‘
3. Next, let's plot the graph using the code given here:
fig, ax = plt.subplots()
ax.plot(df_power.loc[start:end, 'Solar'],
ax.plot(df_power.loc[start:end, 'Solar'],
marker='.', linestyle='-', linewidth=0.5, label='Daily')
ax.plot(power_weekly_mean.loc[start:end, 'Solar'],
marker='o', markersize=8, linestyle='-', label='Weekly Mean
Resample')
ax.set_ylabel('Solar Production in (GWh)')
ax.legend();

32. The output of the preceding code is given here:
The preceding screenshot shows that the weekly mean
time series is increasing over time and is much smoother
than the daily time series.