Week 2: Time Series Forecasting

sktime: Unified Python library for time series machine learning

Installing sktime from PyPI

To install sktime with core dependencies, excluding soft dependencies, via pip type:

pip install sktime

To install sktime with maximum dependencies, including soft dependencies, install with the all_extras modifier:

pip install sktime[all_extras]

Extracted from:

https://www.sktime.org/en/stable/installation.html

import numpy
import matplotlib.pyplot as plt

mean = 0
std = 1 
num_samples = 1000
samples = numpy.random.normal(mean, std, size=num_samples)
samples

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Week 2: Time Series Forecasting

Week 2: Time Series Forecasting
sktime: Unified Python library for time series machine learning
Probabilistic time series model
Mean function
Covariance function
Autocovariance function
Weekly stationary
Strict stationarity of a time series
Simple time series models
1. iid noise
import numpy import...
Slide 12
2. White noise
3. Random walk
Question
Identifying non-stationarity in the mean
Elimination of Trend and Seasonality by Differencing
Backshift notation:
Ordinary differencing
Seasonal differencing
Twice-differenced...
Linear filter model
Autoregressive models
Question
Stationary condition for AR(P)
Stationarity Conditions for AR(1)
Hence, \[\psi(B)=(1-\phi_1...
Geometric series
AR(1) process
slides: https://tsforecasting-thiyanga.netlify.app/slides/timeseries2.html#27...