Time Series Analysis in MS Excel

Time series data is a collection of data points that are ordered by time and show how a variable changes over time.

Analyzing time series data to uncover patterns and trends for future prediction and decision-making.

The pattern of repeating trends in time series data that occur at regular intervals. E.g., Seasonal sales increases during holidays.

Unpredictable fluctuations in time series data, usually caused by random events.

The overall direction of the time series data, which could be upward, downward, or stationary.

Making predictions about future values based on historical patterns in time series data.

Ensuring that the time series data is accurate, consistent, and ready for analysis.

Examining the time series data to understand its characteristics and identify any potential issues.

Represents the central tendency of the data, calculated by summing all values and dividing by the number of observations.

The middle value in a dataset when it is ordered. It is less sensitive to extreme values than the mean.

Measures the dispersion or variability of data points around the mean. A higher value indicates greater variability.

Describes the asymmetry of the distribution. Positive skewness indicates a longer right tail, while negative skewness implies a longer left tail.

Measures the "tailedness" of the distribution. A higher kurtosis suggests heavier tails, potentially indicating more extreme values.

A technique used to break down a time series into its constituent components: trend, seasonality, and noise.

The long-term movement or direction in the time series. It represents the underlying growth or decline in the data.

The repetitive and predictable patterns that occur at fixed intervals within the time series. Often corresponds to daily, weekly, or yearly cycles.

Removing duplicate records from a dataset to ensure data accuracy and prevent skewed analysis.

Identifying missing values in a dataset and understanding their patterns and extent to inform imputation strategies.

Using various techniques to fill in missing values based on the data type and patterns of missingness. Common methods include mean/median imputation, forward/backward filling, and time-series specific algorithms.

Identifying unusual data points in a time series that may be significantly different from other data points, potentially indicating errors or important events.

Choosing a method to handle outliers, such as transforming them, removing them completely, or capping extreme values. The decision should align with the specific analysis goals and the nature of the outliers.

Checking for gaps, overlaps, or inconsistencies in the time sequence of a time series data and ensuring a consistent frequency by adjusting timestamps or interpolating missing points.

Breaking down a time series into its underlying components, such as seasonal variations, trends, and random noise, to understand the contributing factors to the overall pattern.

Keeping a detailed record of every step taken during data preparation, including data cleaning, transformations, and imputation strategies, for reproducibility and effective communication.

A forecasting technique used to smooth out fluctuations in historical data by calculating the average of a specified number of past data points.

A forecasting method that uses a weighted average of past observations, giving more weight to recent data points.

A common metric that measures the average absolute difference between actual and forecasted values, giving a straightforward understanding of the forecast's error.

A more strict error metric that penalizes larger errors more heavily by squaring the difference between actual and forecasted values.

A percentage-based error metric that calculates the average percentage difference between actual and forecasted values.

A parameter in exponential smoothing that determines the influence of past observations on the forecast. A higher damping factor gives more weight to recent observations.

A tool in Excel that simplifies forecasting by automatically generating a forecast sheet based on historical data and selecting the most appropriate forecasting method.

A forecasting method that uses historical data to predict future values. This method is mainly applicable to time-series data.