Prerequisites | Forecasting Bike Sales Revenue | Performing Forecasts Using Multiple Models | Multiple Models Concept | Multiple Model Implementation | Inspecting the Model Fit | sw_tidy | sw_glance | sw_augment | Forecasting the model | Tidying the forecast | Recap

Prerequisites | Forecasting Monthly Bike Sales Revenue | Forecasting Workflow | Step 1: Coerce to a ts object class | Step 2: Modeling a time series | sw_tidy | sw_glance | sw_augment | sw_tidy_decomp | Step 3: Forecasting the model | Step 4: Tidy the forecast object | Recap

Overview | Prerequisites | 1.0 Retrieve Consolidated Symbol Data | 1.1 Stock Indexes | 1.2 Stock Exchanges | 1.0 Get Quantitative Data | 2.1 Yahoo! Finance | 2.2 FRED Economic Data | 2.3 Nasdaq Data Link (Quandl) API | Authentication | Search | Getting Nasdaq Data Link Data | 2.4 Tiingo API | Getting Tiingo Data | 2.5 Alpha Vantage API | Getting Alpha Vantage Data | 2.6 Bloomberg | Getting Bloomberg Data | 3.0 Mutate Quantitative Data | 3.1 Transmute Quantitative Data, tq_transmute | Working with non-OHLC data | 3.2 Mutate Quantitative Data, tq_mutate | Mutate rolling regressions with rollapply | 3.3 _xy Variants, tq_mutate_xy and tq_transmute_xy | Mutate with two primary inputs

Overview | 1.0 Key Concepts | 2.0 Quick Example | 3.0 Workflow | 3.1 Individual Assets | Step 1A: Get stock prices | Step 2A: Mutate to returns | Step 3A: Aggregate to Portfolio Returns (Skipped) | Step 4: Analyze Performance | 3.2 Portfolios (Asset Groups) | Single Portfolio | Steps 1A and 2A: Asset Period Returns | Steps 1B and 2B: Baseline Period Returns | Step 3A: Aggregate to Portfolio Period Returns | Method 1: Aggregating a Portfolio using Vector of Weights | Method 2: Aggregating a Portfolio using Two Column tibble with Symbols and Weights | Step 3B: Merging Ra and Rb | Step 4: Computing the CAPM Table | Multiple Portfolios | Steps 1 and 2 are the Exact Same as the Single Portfolio Example | Step 3A: Aggregate Portfolio Returns for Multiple Portfolios | Steps 3B and 4: Merging and Assessing Performance | 4.0 Available Functions | 4.1 table.Stats | 4.2 table.CAPM | 4.3 table.AnnualizedReturns | 4.4 table.Correlation | 4.5 table.DownsideRisk | 4.6 table.DownsideRiskRatio | 4.7 table.HigherMoments | 4.8 table.InformationRatio | 4.9 table.Variability | 4.10 VaR | 4.11 SharpeRatio | 5.0 Customizing using the ... | 5.1 Customizing tq_portfolio | 5.2 Customizing tq_performance

Panel Data Tutorial Overview | Libraries | Data | Data Preparation | Modeling | Recipe | Models | Prophet | XGBoost | Prophet Boost | Organize in a Modeltime Table | Assess a Single Resample Split | Quantifying Prediction Error Over Time | Apply Models to Resamples | Evaluate Resample Accuracy | Resample Accuracy Plot | Resample Accuracy Table | Model Selection | Wrapup

Overview | Prerequisites | Chart Types | Line Chart | Bar Chart | Candlestick Chart | Charting Multiple Securities | Visualizing Trends | Moving Averages | Example 1: Charting the 50-day and 200-day simple moving average | Example 2: Charting exponential moving averages | Example 3: Charting moving averages for multiple stocks at once | Bollinger Bands | Example 1: Applying BBands using a SMA | Example 2: Modifying the appearance of Bollinger Bands | Example 3: Adding BBands to multiple stocks | ggplot2 Functionality | Example 1: Log Scale with scale_y_log10 | Example 2: Regression trendlines with geom_smooth | Example 3: Charting volume with geom_segment | Themes | Dark

2-Minutes To Tidyquant | Benefits | A Few Core Functions with A Lot of Power | Integrates the Quantitative Analysis Functionality of xts/zoo, quantmod TTR and Performance Analytics | Designed for the data science workflow of the tidyverse | Implements ggplot2 Functionality for Financial Visualizations | Performance Analysis of Asset and Portfolio Returns

Overview | Prerequisites | 1.0 Function Compatibility | zoo Functionality | xts Functionality | quantmod Functionality | TTR Functionality | PerformanceAnalytics Functionality | 2.0 Quantitative Power In Action | Example 1: Use quantmod periodReturn to Convert Prices to Returns | Example 1A: Getting and Charting Annual Returns | Example 1B: Getting Daily Log Returns | Example 2: Use xts to.period to Change the Periodicity from Daily to Monthly | Without Periodicity Aggregation | With Monthly Periodicity Aggregation | Example 3: Use TTR runCor to Visualize Rolling Correlations of Returns | Example 4: Use TTR MACD to Visualize Moving Average Convergence Divergence | Example 5: Use xts apply.quarterly to Get the Max and Min Price for Each Quarter | Example 6: Use zoo rollapply to visualize a rolling regression | Example 7: Use Return.clean and Return.excess to clean and calculate excess returns

Overview | Prerequisites | 1.0 Scaling the Getting of Financial Data | Method 1: Map a character vector with multiple stock symbols | Method 2: Map a tibble with stocks in first column | Method 2A: Make a tibble | Method 2B: Use index or exchange | 2.0 Scaling the Mutation of Financial Data | 3.0 Modeling Financial Data using purrr | Example: Applying a Regression Model to Detect a Positive Trend | Analyze a Single Stock | Scale to Many Stocks | 4.0 Error Handling when Scaling | Pros and Cons to Built-In Error-Handling | Bad Apples Fail Gracefully, tq_get

Single Time Series | Getting Started Setup | Step 1 - Make a Cross-Validation Training Plan | Step 2 - Make a Modeltime Table | Step 3 - Generate Resample Predictions | Step 4 - Evaluate the Results | Accuracy Plot | Accuracy Table | Wrapup

What is a Recursive Model? | Why is Recursive needed for Autoregressive Models? | Single Ensemble Recursive Example | Panel Ensemble Recursive Example | Summary | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting

Time Series Ensemble Forecasting Example | Libraries | Collect the Data | Perform Train / Test Splitting | Modeling | Recipe | Model 1 - Auto ARIMA | Model 2 - Prophet | Model 3 - Elastic Net | Modeltime Workflow for Ensemble Forecasting | Step 1 - Create a Modeltime Table | Step 2 - Make an Ensemble | Step 3 - Forecast! (the Test Data) | Step 4 - Refit on Full Data & Forecast Future | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting

What is Nested Forecasting? | What is Nested Ensembling? | Nested Ensemble Tutorial | Libraries | Data | Prepare the Data in Nested Format | Nested Modeltime Workflow | Step 1A: Create Tidymodels Workflows | Prophet | XGBoost | Step 1B: Nested Modeltime Tables | Accuracy Check | Step 1C: Make Ensembles | Average Ensemble | Weighted Ensemble | Step 2: Select Best | Extract Nested Best Model Report | Extract Nested Best Test Forecasts | Step 3: Refitting and Future Forecast | Extract Nested Future Forecast | Summary | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting

Data | Visualization | Anomalize: breakdown, identify, and clean in 1 easy step | Anomaly Visualization 1: Seasonal Decomposition Plot | Anomaly Visualization 2: Anomaly Detection Plot | Anomaly Visualization 3: Anomalies Cleaned Plot | Learning More

Prerequisites | Bike Sales | Performing Forecasts on Groups | Forecasting Workflow | Step 1: Coerce to a ts object class | mutate and map | Step 2: Modeling a time series | sw_tidy | sw_glance | sw_augment | sw_tidy_decomp | Step 3: Forecasting the model | Step 4: Tidy the forecast | Recap

1. Generating Time Series Analysis Remainders | 1.A. STL | 1.B. Twitter | 1.C. Comparison of STL and Twitter Decomposition Methods | 1.D. Transformations | 2. Detecting Anomalies in the Remainders | 2.A. IQR | 2.B. GESD | 2.C Comparison of IQR and GESD Methods | 3. Conclusion | 4. References | Interested in Learning Anomaly Detection?

Anomalize Intro on YouTube | 5-Minutes To Anomalize | Parameter Adjustment | Adjusting Decomposition Trend and Seasonality | Local Parameter Adjustment | Global Parameter Adjustement | Adjusting Anomaly Detection Alpha and Max Anoms | Alpha | Max Anoms | Further Understanding: Methods | Interested in Learning Anomaly Detection?

Example - Reducing Forecasting Error by 32% | Forecasting Lubridate Downloads | Workflow for Cleaning Anomalies | Before Cleaning with anomalize | After Cleaning with anomalize | 32% Reduction in Forecast Error | Interested in Learning Anomaly Detection?

Introduction | Prerequisites | Data | Time Series Index | Time Series Signature | Get Functions - Turning an Index into Information | Augment Functions (Adding Many Features to a Data Frame) | Time Series Summary | Learning More

Prerequisites | Data | Applications | Automatic Frequency & Trend Selection | Specifying a Frequency or Trend | Frequency | Trend | Time Scale Template | Learning More

Introduction | Prerequisites | Data | Case Study: Conversion issues with ts() | Problems | Solution | Advantages of conversion with tk_tbl() | Conversion Methods | From tbl | to xts | to zoo | to zooreg | to ts | To tbl | From xts | From zoo | From zooreg | From ts | Testing if an object has a timetk index | tk_ts() | Testing other data types | Working with zoo::yearmon and zoo::yearqtr index | Learning More

Introduction | Prerequisites | Data | Train / Test | Modeling | Recipe Preprocessing Specification | Model Specification | Workflow | Training | Hyperparameter Tuning | Forecasting with Modeltime | Modeltime Table | Calibration | Forecast (Testing Set) | Accuracy (Testing Set) | Refit and Forecast Forward | Summary | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting

Libraries | Plotting Time Series | Plotting Groups | Visualizing Transformations & Sub-Groups | Static ggplot2 Visualizations & Customizations | Box Plots (Time Series) | Regression Plots (Time Series) | Summary | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting

Libraries | Data | Summarize by Time | Period Summarization | Period Smoothing | Filter By Time | Time Range Filtering | Padding Data | Fill in Gaps | Low to High Frequency | Sliding (Rolling) Calculations | Rolling Mean | Rolling Regression | Learning More

Prerequisites | Making a Time Series Sequence | Future Time Series Sequence | Weekends & Holidays | Learning More

Libraries | Data | TS Features | Clustering with K-Means | Visualize the Cluster Assignments | Learning More

Libraries | Correlation Plots | Grouped ACF Diagnostics | Grouped CCF Plots | Seasonality | Seasonal Visualizations | Grouped Seasonal Visualizations | STL Diagnostics | Learning More

Introducing as_period() | Datasets required | Daily to monthly | Generic periods | Details and the start_date argument | The side argument | Grouped datasets

Package motivation | Index manipulation | Multiple series

Introducing rollify() | Datasets required | A rolling average | Purrr functional syntax | Optional arguments | Returning more than 1 value per call | Custom missing values | Rolling regressions

Problem | Solution | Main Benefits | Correlation Funnel Process | Example - Customer Churn | Step 1 - Prepare Data as Binary Features | Step 2 - Correlate to the Target | Step 3 - Plot the Correlation Funnel | Business Insights | Conclusion | More Information

Methodology | Key Considerations | Prior to Binarization Step | During Binarization | Prior to Correlation Step | After Plotting the Correlation Funnel | FAQs | 1. How does the Correlation Funnel Find Relationships in Numeric Data? | 1.1 Linear Relationships | 1.2 Non-Linear Relationships | 2. What About Skewed Numeric Data? | 2.1 Skewed Data | 2.2. Highly Skewed Data | 3. How does the Correlation Funnel Work With Categorical Data? | 3.1 One-Hot Encoding vs Dummy Encoding | 3.2 Reducing Dimensionality (Preventing Irrelevant Factor Levels) | 3.3 Assessing Correlations With Categorical Data | References

Introducing filter_time() | Datasets required | Year filtering example | Month filtering example | Keywords | Grouped example | Finer periods | [ syntax | Using variables in the filter