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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.