ubuntu2204
Kernel: Python 3 (system-wide)
In [5]:
In [6]:
Out[6]:
2.0.1+cu117
False
In [0]:
In [0]:
Next, we will use the SimpleExpSmoothing() function to create the model and fit it to our time series data.
In [4]:
Out[4]:
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║ ║
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╟───────────────────────────────────────────────────────────────╢
║ Note: You can't become 'root' on CoCalc for security reasons. ║
╚═══════════════════════════════════════════════════════════════╝
Package Version
--------------------------------- --------------
3to2 1.1.1
absl-py 1.2.0
access 1.1.8
accumulation-tree 0.6.2
adtk 0.6.2
aeidon 1.10.1
aenum 3.1.11
affine 2.3.1
agate 1.6.3
agentpy 0.1.5
aiohttp 3.8.1
aiosignal 1.2.0
alabaster 0.7.12
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altair 4.2.0
amqp 5.0.9
ansible 6.1.0
ansible-core 2.13.2
ansiwrap 0.8.4
anyio 3.5.0
anyjson 0.3.3
AnyQt 0.1.1
apache-libcloud 3.6.0
appdirs 1.4.4
APScheduler 3.9.1
apsw 3.36.0.post1
arch 5.3.1
argcomplete 1.10.3
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
args 0.1.0
arrow 1.2.2
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ase 3.22.1
asgiref 3.5.2
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asteval 0.9.28
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astroalign 2.4.1
astroid 2.11.7
astroML 1.0.2.post1
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astropy 5.3
astropy-healpix 0.6
astroquery 0.4.6
astroscrappy 1.1.0
asttokens 2.0.5
astunparse 1.6.3
async-generator 1.10
async-timeout 4.0.2
atomicwrites 1.4.0
attrs 22.1.0
audeer 1.18.0
audiofile 1.1.0
audioread 2.1.9
Automat 20.2.0
autopep8 2.0.2
av 9.2.0
avro 1.11.0
aws-requests-auth 0.4.3
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azure-core 1.24.2
azure-storage-blob 12.13.0
Babel 2.10.3
backcall 0.2.0
backoff 2.1.2
bash_kernel 0.9.0
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black 23.3.0
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In [1]:
Out[1]:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_1174/355211648.py in <cell line: 1>()
----> 1 model = sm.tsa.SimpleExpSmoothing(time_series_data).fit()
NameError: name 'sm' is not defined
After the model is fit, we can use the forecast() function to generate values for future time periods. We will generate values for the next 12 months.
In [0]:
Finally, we can create a plot to display the original time series data and the forecasted values. We will use the matplotlib library for this.
In [0]:
The plot generated by the above code will show the original time series data and the forecasted values for the next 12 months.