This document discusses time series forecasting. It defines a time series as observations of a variable over time and notes they are used in fields like statistics, finance, and weather forecasting. It describes the main components that influence time series as secular trends, seasonal variation, cyclical variation, and irregular variation. It provides examples of each component and how they can impact a time series. It also includes an example Python code for performing time series forecasting using an autoregressive model.

1. Time Series Forecasting
Prepared by:
Omar Al-dabash
Computer Engineering Department

2. Time series forecasting
 A time series is a set of observations on the values that a variable
takes at different times.
 Time series are used in statistics, econometrics, mathematical
finance, weather forecasting, earthquake predication and many
other application.

3. Component of time series
 The change which are in time series, they are effected by economic, social,
natural, Industrial & Political Reasons. These reasons are component of time
series.
• Secular Trend.
• Seasonal variation.
• Cyclical Variation.
• Irregular variation.

4. Secular Trend.
 The increase or decrease in the movements of a time series is called Secular Trend.
A time series data may show upward trend or downward trend for a period of years and
this may be due to factors like.
• Increase in population.
• Change in technological progress.
• Large scale shift in consumers demands.

5. Seasonal variation
 Seasonal variation is a short- term fluctuation in a time series which occur
periodically in year.
• The weather conditions
 Cyclical Variation
Cyclical Variations are recurrent upward or downward movements in the
time series but the period of cycle is greater than a year.

6. Irregular variation
 Irregular variation are fluctuations in time series that are short
duration, erratic in natural and follow no regularity in the occurrence
pattern.
 The Irregular fluctuations results due to the occurrence unforeseen
event like floods, earthquakes, wars and famines.

7. Example
from pandas import Series
from matplotlib import pyplot
from statsmodels.tsa.ar_model import AR
from sklearn.metrics import
mean_squared_error
import numpy
# create a difference transform of the dataset
def difference(dataset):
diff = list()
for i in range(1, len(dataset)):
value = dataset[i] - dataset[i - 1]
diff.append(value)
return numpy.array(diff)
def predict(coef, history):
# Make a prediction give regre
yhat = coef[0]
for i in range(1, len(coef)):
yhat += coef[i] * history[-i]
return yhat
series = Series.from_csv('daily-total-female-