"Seasonality is the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly."
The presence of patterns that repeat at fixed intervals of time.
Time Series Data: Understanding what is time series data, and how it differs from cross-sectional data.
Stationarity: The concepts of stationary and nonstationary time series, and how to test for it.
Autocorrelation: The presence of autocorrelation (serial correlation) in time series data, and how to detect it.
ARIMA Models: Introduction to ARIMA models, selecting appropriate ARIMA order (p, d, q) for time series based on ACF and PACF plots.
Seasonality: What is seasonal variation, how to detect seasonal patterns, and how to measure it.
Seasonal Decomposition: Time series decomposition into trend, seasonal, and residual components via additive or multiplicative models.
Time Series Forecasting: Forecasting future values of time series using ARIMA or other models.
Time Series Smoothing: Moving averages and other techniques for smoothing out the noise in time series data.
Exponential Smoothing: Various exponential smoothing techniques such as simple exponential smoothing, Holt's linear smoothing, and Holt-Winters method.
Fourier Analysis: The Fourier transform, its applications in signal processing and Fourier series.
Spectral Analysis: Estimation of the power spectrum of a time series, in order to identify the periodic components.
Seasonal Adjustment and Modeling: Different techniques for seasonal adjustment of time series data, such as X-11, SEATS, and ARIMA-based models.
Time Series Simulation: Generating synthetic time series data, based on stochastic processes such as ARIMA or GARCH models.
Machine Learning for Time Series: Supervised and unsupervised learning techniques for time series analysis, such as clustering, classification, and regression.
Neural Networks for Time Series: Modeling a time series using different types of neural networks, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks.
Multivariate Time Series: Dealing with time series data that have multiple inter-related variables.
Time Series Data Visualization: Exploring and visualizing time series data using various plotting and visualization techniques.
Time Series Anomaly Detection: Detecting abnormal or anomalous behavior in time series data using statistical methods or machine learning.
Time Series Clustering: Grouping similar time series data together based on various similarity measures.
Time Series Compression: Reducing the amount of storage space required for time series data, while still maintaining important information.
Additive seasonal variation: Additive seasonal variation is one of the most common types of seasonality. In this type of seasonality, the seasonal variations are added to the level of the time series.
Multiplicative seasonal variation: In multiplicative seasonal variation, the seasonal variations are multiplied by the level of the time series. This type of seasonality is commonly observed in financial data.
Calendar variation: Calendar variation is the seasonal variation caused by the calendar, i.e., the number of days in a month or the presence of holidays in a week. For example, the sales of a retail store may increase during the Christmas holidays every year.
Trading day variation: Trading day variation is the seasonal variation caused by the trading days in a week. The sales of a retail store may increase on weekends, and therefore, there is a trading day variation in the time series.
Moving holiday variation: Moving holiday variation is the seasonal variation caused by the holidays that move around each year. For example, Easter may fall on different dates every year, causing a moving holiday variation.
Autoregressive seasonality: Autoregressive seasonality occurs when the seasonality pattern is similar in the previous year or previous period.
Non-autoregressive seasonality: Non-autoregressive seasonality occurs when the pattern of seasonality changes over time and is not similar to previous periods or years. This type of seasonality is common in volatile economic sectors like agriculture.
Harmonic seasonality: Harmonic seasonality is a type of seasonality pattern where the seasonal variations repeat at regular intervals.
Consecutive patterns seasonality: Consecutive patterns seasonality is a type of seasonality pattern where the seasonal variations occur in consecutive periods or seasons.
Sudden change in seasonality: Sudden change in seasonality is a type of seasonality pattern where the seasonal pattern abruptly changes due to external factors like sudden changes in consumer demand or introduction of new products.
"Seasonality may be caused by various factors, such as weather, vacation, and holidays."
"Seasonal fluctuations in a time series can be contrasted with cyclical patterns. The latter occur when the data exhibits rises and falls that are not of a fixed period."
"Such non-seasonal fluctuations are usually due to economic conditions and are often related to the 'business cycle.'"
"It is necessary for organizations to identify and measure seasonal variations within their market to help them plan for the future. This can prepare them for the temporary increases or decreases in labor requirements and inventory as demand for their product or service fluctuates over certain periods."
"Apart from these considerations, the organizations need to know if the variation they have experienced has been more or less than the expected amount, beyond what the usual seasonal variations account for."
"Organizations facing seasonal variations, such as ice-cream vendors, are often interested in knowing their performance relative to the normal seasonal variation."
"Seasonal variations in the labor market can be attributed to the entrance of school leavers into the job market as they aim to contribute to the workforce upon the completion of their schooling."
"Their focus is on how unemployment in the workforce has changed, despite the impact of the regular seasonal variations."
"This may require training, periodic maintenance, and so forth that can be organized in advance."
"The presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly."
"Cyclical patterns occur when the data exhibits rises and falls that are not of a fixed period."
"Seasonality may be caused by various factors, such as weather, vacation, and holidays."
"Their period usually extends beyond a single year, and the fluctuations are usually of at least two years."
"It is necessary for organizations to identify and measure seasonal variations within their market to help them plan for the future."
"Organizations facing seasonal variations, such as ice-cream vendors, are often interested in knowing their performance relative to the normal seasonal variation."
"Seasonal variations in the labor market can be attributed to the entrance of school leavers into the job market."
"Their focus is on how unemployment in the workforce has changed, despite the impact of the regular seasonal variations."
"This may require training, periodic maintenance, and so forth that can be organized in advance."
"The organizations need to know if the variation they have experienced has been more or less than the expected amount, beyond what the usual seasonal variations account for."