Advanced Financial Modelling, Quantitative Finance and Econometrics · Physical, Online
Time-Series Analysis & Financial Forecasting Using Excel, SPSS, Stata and Power BI Training Workshop
5 Days2 sessions available Certificate on completion
The Time-Series Analysis & Financial Forecasting Using Excel, SPSS, Stata and Power BI Training Workshop is a practical, software-based programme designed to equip participants with the skills to analyse historical financial and economic data, identify trends and seasonal patterns, build forecasting models, evaluate predictive accuracy, and communicate results for financial planning and decision-making.
The workshop combines financial analysis, statistical forecasting, econometric modelling, and business intelligence reporting. Participants will work with realistic datasets to forecast revenue, expenditure, cash flows, banking performance, loan portfolios, inflation, exchange rates, interest rates, sales, and investment-related indicators.
Course objectives
Prepare and analyse time-series data: Clean historical financial and economic datasets, identify trends, seasonality, cyclical movements, outliers, missing observations, and structural changes. Apply statistical forecasting techniques: Develop forecasts using moving averages, exponential smoothing, trend regression, ARIMA, and seasonal forecasting methods in the appropriate software. Conduct financial and economic forecasting: Build forecasts for revenue, expenditure, cash flow, sales, deposits, loans, inflation, interest rates, exchange rates, and other financial indicators. Communicate forecasts for decision-making:
Develop forecasting reports and interactive Power BI dashboards that explain expected outcomes, financial risks, uncertainty, and implications for planning.
Evaluate forecasting accuracy and reliability: Compare alternative models using appropriate error metrics, holdout samples, backtesting, residual diagnostics, and prediction intervals.
Prepare and analyse time-series data: Clean historical financial and economic datasets, identify trends, seasonality, cyclical movements, outliers, missing observations, and structural changes.
Communicate forecasts for decision-making: Develop forecasting reports and interactive Power BI dashboards that explain expected outcomes, financial risks, uncertainty, and implications for planning.
Apply statistical forecasting techniques: Develop forecasts using moving averages, exponential smoothing, trend regression, ARIMA, and seasonal forecasting methods in the appropriate software.
Who should attend
Financial Analysts, Economists, Banking Professionals, Investment Analysts, Accountants, Finance Managers, Treasury Officers, Financial Controllers, Budget Officers, Risk Analysts, Credit Analysts, Data Analysts, Business Analysts, Statisticians, Researchers, Data Scientists, Financial Planners, Portfolio Managers, Equity Research Analysts, Business Intelligence Professionals, Corporate Planning Officers, Project Finance Analysts, Investment Managers, Revenue Forecasting Specialists, Monitoring and Evaluation Professionals, Public Finance Officers, Policy Analysts, and professionals involved in financial planning, economic research, statistical analysis, or business forecasting.
Course content
Module 1: Foundations of Time-Series Analysis and Financial Forecasting Introduction to time-series data and its applications in finance, banking, economics, business planning, and investment analysis, cross-sectional data versus time-series data, daily,
Module 2: Financial Data Collection, Cleaning and Preparation Using Excel Importing financial datasets from Excel workbooks, CSV files, banking reports, accounting systems, and statistical datasets, standardising date formats, sorting observations chrono
Module 3: Exploratory Time-Series Analysis and Visualisation Analysing historical financial movements, constructing line charts and trend plots, calculating averages and growth rates, identifying upward and downward trends, examining seasonal fluctuation
Module 4: Trend Analysis, Seasonality and Time-Series Decomposition Long-term trends, seasonal patterns, cyclical movements, irregular variations, additive and multiplicative decomposition, monthly and quarterly seasonal indices, trend estimation, season
Module 5: Financial Growth Rates, Moving Averages and Smoothing Absolute and percentage changes, month-on-month and year-on-year growth, compound annual growth rates, simple and weighted moving averages, rolling averages, simple exponential smoothing, tr
Module 6: Financial Forecasting with Microsoft Excel Building financial forecasting worksheets, applying trendlines and regression-based forecasts, using FORECAST.LINEAR and FORECAST.ETS, generating forecast tables and charts, working with consistent tim
Module 7: Statistical Time-Series Forecasting with IBM SPSS Statistics Preparing time-indexed data in SPSS, defining the periodicity of observations, creating and interpreting time-series plots, identifying trends and seasonal patterns, applying exponent
Module 8: Time-Series Econometrics and Forecasting with Stata Declaring time-series data, defining time variables and frequencies, generating time-series plots, calculating lags and differences, examining autocorrelation, conducting Augmented Dickey-Full
Module 9: Regression-Based and Driver-Based Financial Forecasting Using explanatory variables to forecast financial outcomes, simple and multiple regression, interpreting coefficients, lagged predictors, relationships between revenue and business activit
Module 10: ARIMA and Seasonal ARIMA Forecasting Autoregressive models, moving-average models, integrated models, ARIMA specifications, differencing, model identification, autocorrelation and partial autocorrelation, model selection, seasonal ARIMA, param
Module 11: Financial Forecasting Applications in Banking and Finance Forecasting monthly deposits, loan disbursements, loan repayments, interest income, fee income, operating expenditure, non-performing loan ratios, liquidity indicators, revenue, working
Module 12: Forecast Accuracy, Model Diagnostics and Backtesting Splitting historical data into estimation and testing periods, chronological validation, rolling-origin backtesting, comparing forecasts with actual observations, mean absolute error (MAE),
Module 13: Financial Risk, Volatility and Scenario Analysis Understanding financial returns and volatility, rolling standard deviation, periods of unusually high variability, changes in economic conditions, interest-rate shocks, inflation scenarios, fore
Module 14: Financial Forecasting Dashboards with Microsoft Power BI Importing and transforming financial data, developing a suitable date table, creating financial measures, building time-based line charts, comparing actual and forecast values, analysing
Module 15: Integrated Financial Forecasting Project and Management Reporting Participants will complete an end-to-end forecasting exercise using a realistic financial dataset. They will clean the data, conduct exploratory analysis, select suitable foreca
Sample Practical Training Scenarios Scenario 1: Forecasting Monthly Bank Deposits Business problem: A commercial bank needs to estimate deposit volumes for the next 12 months to support liquidity planning and business growth targets. Sample dataset: Mon
General notes
The course uses four complementary platforms. Microsoft Excel is applied to financial calculations, trend analysis, forecasting formulas, budgeting, and scenario modelling. IBM SPSS Statistics is used for statistical time-series modelling, exponential smoothing, ARIMA, and forecast evaluation. Stata supports econometric analysis, stationarity testing, regression-based forecasting, and financial time-series analysis. Power BI is used to develop interactive dashboards, analyse historical performance, compare actual results with forecasts, and present insights to management.
Participants will learn not only how to generate forecasts, but also how to assess whether those forecasts are reliable, compare alternative methods, interpret uncertainty, and translate analytical findings into practical financial recommendations.
Banking and Deposit Forecasting: Analyse historical bank deposits and loan disbursements to forecast future trends using Excel, SPSS and Stata, then present findings in Power BI.
Corporate Revenue and Cash Flow Forecasting: Use historical sales, expenditure and cash flow data to forecast future revenue, operating costs and liquidity requirements.
Inflation and Interest Rate Forecasting: Analyse historical inflation, interest rates and exchange rates using time-series and econometric models to support financial planning.
Sales and Demand Forecasting: Identify sales trends and seasonal patterns to forecast product demand, estimate future revenue and support inventory and budget planning.
Credit Risk Trend Analysis: Examine historical non-performing loan ratios and repayment trends to identify emerging risks and support credit portfolio monitoring.
Frequently asked questions
The workshop is suitable for financial analysts, economists, accountants, banking professionals, investment analysts, data analysts, statisticians, risk analysts, finance managers, researchers, and professionals involved in budgeting, financial planning, economic analysis, and forecasting.
The workshop uses four main platforms: Microsoft Excel, IBM SPSS Statistics, Stata, and Microsoft Power BI. Excel is used for spreadsheet-based forecasting, SPSS for statistical time-series modelling, Stata for econometric analysis, and Power BI for interactive financial dashboards and reporting.
Participants will explore trend analysis, moving averages, exponential smoothing, regression-based forecasting, ARIMA and seasonal forecasting, time-series diagnostics, and forecast accuracy evaluation. The methods are applied to practical financial and economic datasets
The workshop covers forecasting revenue, sales, expenditure, cash flows, bank deposits, loan disbursements, inflation, interest rates, and exchange rates. Participants also learn to compare forecasts with actual results and communicate findings through financial reports and dashboards.