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TIME SERIES#1

A sequence of data points collected or recorded at specific time intervals, crucial for forecasting.

FORECASTING#2

The process of predicting future values based on historical data, essential in financial analysis.

ARIMA#3

AutoRegressive Integrated Moving Average, a popular model for time series forecasting.

SEASONALITY#4

Patterns that repeat at regular intervals in time series data, indicating predictable fluctuations.

TREND#5

The general direction in which data points move over time, indicating growth or decline.

NOISE#6

Random variations in data that do not follow a pattern, complicating analysis.

DATA PREPARATION#7

The process of cleaning and organizing data to make it suitable for analysis.

MEAN ABSOLUTE ERROR (MAE)#8

A metric for measuring forecast accuracy by averaging absolute errors between predicted and actual values.

ROOT MEAN SQUARE ERROR (RMSE)#9

A common metric to assess the accuracy of a forecasting model by measuring the square root of the average squared differences.

PARAMETER SELECTION#10

Choosing the appropriate values for the components of a model, crucial for effective ARIMA implementation.

TRAINING DATA#11

A subset of data used to train a forecasting model, helping it learn patterns.

TESTING DATA#12

A separate subset of data used to evaluate the performance of a forecasting model.

VISUALIZATION#13

Graphical representation of data to identify trends, seasonality, and patterns easily.

STATIONARITY#14

A property of time series data where statistical properties remain constant over time, important for ARIMA.

DIFFERENCING#15

A technique used to make a time series stationary by subtracting previous observations from current ones.

AUTOCORRELATION#16

The correlation of a time series with a lagged version of itself, used to identify patterns.

LAG#17

A term describing the time delay between two observations in a time series.

FINANCIAL MODELING#18

The process of creating representations of a financial situation to analyze performance and forecast future outcomes.

CASE STUDY#19

An in-depth analysis of a real-world scenario to illustrate practical applications of theoretical concepts.

DATA NORMALIZATION#20

Adjusting values in the dataset to a common scale, improving model performance.

CROSS-VALIDATION#21

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

INVESTMENT DECISION-MAKING#22

The process of evaluating and selecting investment opportunities based on data analysis.

PREDICTIVE ANALYTICS#23

Using statistical techniques and algorithms to analyze historical data and predict future outcomes.

MODEL EVALUATION#24

Assessing the performance of a forecasting model using various metrics to ensure accuracy.

APPLICATIONS IN FINANCE#25

Using time series analysis techniques to make informed decisions in financial markets.