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Questions and Answers
Which of the following best describes a time series?
Which of the following best describes a time series?
- Numerical values measured without any interval.
- Data points indexed in time order. (correct)
- A collection of data points with no specific order.
- Randomly selected observations measured at a single point in time.
What distinguishes time series data from other types of data?
What distinguishes time series data from other types of data?
- Time series data includes only categorical variables.
- Time series data is collected at a single point in time.
- Time series data cannot be used for forecasting.
- Time series data involves observations obtained through repeated measurements over time. (correct)
Which of the following is NOT a characteristic of time series data?
Which of the following is NOT a characteristic of time series data?
- Data is always collected at regular intervals. (correct)
- Observations are collected through repeated measurements.
- Data points are indexed in time order.
- Multiple variables can be recorded simultaneously.
Which of the following is the best example of a time series?
Which of the following is the best example of a time series?
What key elements does a univariate time series consist of?
What key elements does a univariate time series consist of?
What is the defining feature of a multivariate time series compared to a univariate time series?
What is the defining feature of a multivariate time series compared to a univariate time series?
Forecasting the prices of a stock tomorrow is an example of which type of time series?
Forecasting the prices of a stock tomorrow is an example of which type of time series?
Unlike regular time series data, irregular time series data:
Unlike regular time series data, irregular time series data:
What are the key purposes of time series analysis?
What are the key purposes of time series analysis?
In time series analysis, what does 'descriptive' refer to?
In time series analysis, what does 'descriptive' refer to?
Why might it be necessary to convert non-stationary data to stationary data in time series analysis?
Why might it be necessary to convert non-stationary data to stationary data in time series analysis?
What does 'periodicity' refer to in the context of time series data characteristics?
What does 'periodicity' refer to in the context of time series data characteristics?
What is the primary characteristic of seasonality in time series data?
What is the primary characteristic of seasonality in time series data?
What issue does non-linearity address in time series modeling?
What issue does non-linearity address in time series modeling?
What elements are accounted for in Time Series Analysis?
What elements are accounted for in Time Series Analysis?
What is the 'trend' in time series analysis referring to?
What is the 'trend' in time series analysis referring to?
What defines 'seasonality' in a time series?
What defines 'seasonality' in a time series?
What is 'cyclicity' in the context of time series analysis?
What is 'cyclicity' in the context of time series analysis?
In time series analysis, what are 'Irregular/Residual' components?
In time series analysis, what are 'Irregular/Residual' components?
What is a key consideration when dealing with temporal data across different regions?
What is a key consideration when dealing with temporal data across different regions?
What role do regression models play in time series modeling?
What role do regression models play in time series modeling?
How are time series models updated as time progresses?
How are time series models updated as time progresses?
What is the primary goal of forecasting in the context of time series analysis?
What is the primary goal of forecasting in the context of time series analysis?
Which of the following is an example of a forecasting use case?
Which of the following is an example of a forecasting use case?
In the time series model ( Y_t = T_t + S_t + R_t ), what does ( S_t ) represent?
In the time series model ( Y_t = T_t + S_t + R_t ), what does ( S_t ) represent?
What does ( T_t ) signify in the time series model ( Y_t = T_t + S_t + R_t )?
What does ( T_t ) signify in the time series model ( Y_t = T_t + S_t + R_t )?
In the context of the time series model (( Y_t = T_t + S_t + R_t )), what does the term ( R_t ) represent?
In the context of the time series model (( Y_t = T_t + S_t + R_t )), what does the term ( R_t ) represent?
According to the time series model, which of the following business scenarios explains the seasonal term, S ?
According to the time series model, which of the following business scenarios explains the seasonal term, S ?
Which component of the time series model accounts for noise?
Which component of the time series model accounts for noise?
Which of the following statements accurately describes the 'I' component in ARIMA?
Which of the following statements accurately describes the 'I' component in ARIMA?
What does the 'AR' component stand for in the ARIMA model?
What does the 'AR' component stand for in the ARIMA model?
In ARIMA, what is the main function of the Autoregressive (AR) component?
In ARIMA, what is the main function of the Autoregressive (AR) component?
What characterizes the Moving Average (MA) component in the ARIMA model?
What characterizes the Moving Average (MA) component in the ARIMA model?
What is the purpose of integrating in the ARIMA model?
What is the purpose of integrating in the ARIMA model?
What transformation does the 'I' (Integrated) component perform in the ARIMA model to achieve stationarity?
What transformation does the 'I' (Integrated) component perform in the ARIMA model to achieve stationarity?
When is it appropriate to perform decomposition?
When is it appropriate to perform decomposition?
What is the purpose of Universal Time Coordinated (UTC) in the context of time series analysis?
What is the purpose of Universal Time Coordinated (UTC) in the context of time series analysis?
Why are time zones a challenge when dealing with temporal data?
Why are time zones a challenge when dealing with temporal data?
How do time zones complicate the analysis of temporal data collected across different geographic regions?
How do time zones complicate the analysis of temporal data collected across different geographic regions?
A company observes that its sales spike every December due to holiday shopping and then significantly decrease in January. Considering time series components, this is an example of:
A company observes that its sales spike every December due to holiday shopping and then significantly decrease in January. Considering time series components, this is an example of:
What analytical approach is used to break down a time series into its underlying patterns?
What analytical approach is used to break down a time series into its underlying patterns?
How are regression models utilized in time series modeling?
How are regression models utilized in time series modeling?
Consider a time series showing gradual increase in average global temperatures over the past century. This long-term increase represents what component?
Consider a time series showing gradual increase in average global temperatures over the past century. This long-term increase represents what component?
Flashcards
Time Series
Time Series
Data points indexed in time order. A collection of observations obtained through repeated measurements over time.
Time Series
Time Series
A sequence of data in chronological order. An ordered sequence of numerical values measured over spaced time intervals.
Univariate Time Series
Univariate Time Series
Time series with a single variable.
Multivariate Time Series
Multivariate Time Series
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Regular Time Series
Regular Time Series
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Irregular Time Series
Irregular Time Series
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Why use a Time Series
Why use a Time Series
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Stationary Time Series
Stationary Time Series
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Periodicity
Periodicity
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Seasonality
Seasonality
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Non-Linearity
Non-Linearity
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Time Series Analysis
Time Series Analysis
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Trend
Trend
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Seasonality in Time Series
Seasonality in Time Series
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Cyclicity
Cyclicity
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Residuals
Residuals
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Forecasting
Forecasting
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T(t)
T(t)
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S(t)
S(t)
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R(t)
R(t)
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ARIMA
ARIMA
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Study Notes
- Time series data points are indexed in time order
- Time series data is a collection of observations obtained through repeated measurements over time
- A time series is a sequence of data in chronological order
- Can be an ordered sequence of numerical values measured over spaced time intervals
- Data may be collected in regular or irregular intervals
- Multiple variables can be recorded simultaneously
Time Series Examples
- Stock prices
- Sales revenue
- Energy demand and production
- Temperature
Univariate Time Series
- Time series has values and a time index as illustrated in the following example
- 30/03/2024 : 200
- 31/03/2024 : 220
- 01/04/2024 : 230
- 02/04/2024 : 235
Multivariate Time Series
- An example of Multivariate Time Series may involve multiple items over time.
- 30/03/2024: Item 1 = 200, Item 2 = 100, Item 3 = 330, Item 4 = 120
- 31/03/2024: Item 1 = 220, Item 2 = 120, Item 3 = 300, Item 4 = 135
- 01/04/2024: Item 1 = 230, Item 2 = 150, Item 3 = 335, Item 4 = 133
- 02/04/2024: Item 1 = 235, Item 2 = 175, Item 3 = 340, Item 4 = 200
- Forecasting the prices of a stock tomorrow is an example a Univariate Time Series
Time Series Types
- Regular series includes hourly temperature and weekly sales
- Irregular series includes patient records
Why Use Time Series Analysis
- To be descriptive, you can identify trends, seasonality, and variability
- To be predictive, you can use trends, seasonality, and variability to predict what will happen next
- The goal can be to identify the internal structure (descriptive) or to forecast near-term events based on recent history (predictive)
Time Series Data Characteristics
- Stationary vs. Non-Stationary
- Statistical properties such as mean, variance and autocorrelation are relatively stable over time
- For forecasting, one might need to convert non-stationary data to stationary
- Periodicity is a pattern in a time series that occurs at regular time intervals or is cyclical
- Seasonality accounts for seasonal variations with cycles that repeat regularly over time
- Non-Linearity: Simple linear time series models typically leave certain aspects of economic and financial data unexplained, non-linearity is needed to account for structural and behavioral changes
Time Series Analysis
- Accounts for the internal structure taken over time
- Includes trends, seasonality, cycles and irregular patterns
Time Series - Trend
- A trend is a long-term change in the mean of the time series
- The trend is a long-term movement in a time series
- Is the underlying direction (upward or downward) and rate of change in a time series when allowance has been made for the other components
Time Series - Seasonality
- Seasonality consists of regular, repetitive fluctuations
- Seasonal fluctuations of known periodicity are the component of variation in a time series that is dependent on the time of the year
- They describe any regular fluctuations with a period of less than one year
- The costs of various types of fruits and vegetables and average daily rainfall all show marked seasonal variation
Time Series - Cyclic
- Cyclicity refers to irregular fluctuations over longer time periods
- They consist of cyclical variations of non-seasonal nature, whose periodicity is unknown
- Economic recessions and housing markets are examples of cyclic time series
Time Series - Irregular/Residual
- Residuals represent the error term, and irregular fluctuations
- These are random or chaotic noisy residuals left over when other components of the series (trend, seasonal and cyclical) have been accounted for
- Trend and seasonality, though conceptually distinct, are essentially entangled
- Decomposition is not always possible
- Challenges that are specific to dealing with temporal data include time zones and daylight savings time
Universal Time Coordinated (UTC)
- Visit https://www.timeanddate.com/time/aboututc.html for more information
Time Series Modeling
- Time series models are data models that enable analysis of data points and observations over time
- Regression models can be used to make predictions about future trends
- Additional data points can be incorporated into the models as feedback loops as time passes
Forecasting
- Forecasting is the process of predicting future values of the time series through known past values plus any other related data available at the time of forecast
Forecasting Use Cases
- To forecast next month's sales based on the last few months
- To forecast tomorrow's stock price based on the last few days
- To forecast power demand in the near term based on the last few days
Time Series Modeling
- The time series can be modelled as: Yt =Tt +St +Rt, t=1,...,n, where
- T₁ = Trend term representing the sales of iPads steadily increased over the last few years: trending upward
- S₁ = The seasonal term (short term periodicity) exemplified by retail sales that fluctuate in a regular pattern over the course of a year
- Typically, sales increase from September through December and decline in January and February
- R₁ = Random fluctuation due to noise, or regular high frequency patterns in fluctuation
ARIMA
- Autoregressive Integrated Moving Average Model
- AR (Autoregressive): Later values are predicted by earlier, lagged values
- I (Integrated): Absolute values are replaced by differences in values (the deltas)
- MA (Moving Average): Regression errors are a linear combination of current and previous values
Pycaret
- Visit https://pycaret.org/ for more information
Stumpy
- Visit https://stumpy.readthedocs.io for more information
Time series datasets
- Visit https://fred.stlouisfed.org/tags/series for more information
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