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HOW WOULD THE STUDENTS EVALUATE THE ACCURACY OF THE DIFFERENT FORECASTING MODELS

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The students would need to obtain historical data on the variable they are trying to forecast. This could be things like past monthly or quarterly sales figures, stock prices, weather data, or other time series data. They would split the historical data into two parts – a training set and a testing set.

The training set would contain the earliest data and would be used to develop and train each of the forecasting models. Common models students may consider include simple exponential smoothing, Holt’s linear trend method, Brown’s exponential smoothing approach, ARIMA (autoregressive integrated moving average) models, and regression models with lagged predictor variables. For each model, the students would select the optimal parameters like the alpha level in simple exponential smoothing or the p, d, q parameters in ARIMA.

Once the models have been developed on the training set, the students would then forecast future periods using each model but only using the information available up to the end of the training set. These forecasts would be compared to the actual data in the testing set to evaluate accuracy. Some common metrics that could be used include:

Mean Absolute Percentage Error (MAPE) – This calculates the average of the percentage errors between each forecast and the actual value. It provides an easy to understand measure of accuracy with a lower score indicating better forecasts.

Mean Absolute Deviation (MAD) – Similar to MAPE but without calculating the percentage, instead just looking at the average of the absolute errors.

Mean Squared Error (MSE) – Errors are squared before averaging so larger errors are weighted more heavily than small errors. This focuses evaluation on avoiding large forecast misses even if some smaller errors occur. MSE needs to be interpreted carefully as the scale is not as intuitive as MAPE or MAD.

Mean Absolute Scaled Error (MASE) – Accounts for the difficulty of the time series by comparing forecast errors to a naive “random walk” forecast. A MASE below 1 indicates the model is better than the naive forecast.

The students would calculate accuracy metrics like MAPE, MAD, MSE, and MASE for each model over the test period forecasts. They may also produce graphs to visually compare the actual values to each model’s forecasts to assess accuracy over time. Performance could also be evaluated at different forecast horizons like 1-period ahead, 3-period ahead, 6-period ahead forecasts to see if accuracy degrades smoothly or if some models hold up better farther into the future.

Additional analysis may include conducting Diebold-Mariano tests to statistically compare model accuracy and determine if differences in the error metrics between pairs of models are statistically significant or could be due to chance. They could also perform residual diagnostics on the forecast errors to check if any patterns remain that could be exploited to potentially develop an even more accurate model.

After comprehensively evaluating accuracy over the test set using multiple error metrics and statistical comparisons, the students would identify which forecasting model or models provided the most accurate and reliable forecasts based on the historical data available. No single metric alone would determine the best model, but rather the preponderance of evidence across the board in terms of MAPE, MAD, MSE, MASE, visual forecasts, statistical tests, and residual analysis.

The students would report their analysis, including details on developing each model type, describing the accuracy metrics calculated, presenting the results visually through tables and graphs, discussing their statistical findings, and making a conclusion on the most accurate model indicated by this thorough ex-post evaluation process. This would provide them significant insight into forecasting, model selection, and evaluation that they could apply in practice when working with real time-series data challenges.

While accuracy alone cannot guarantee a model’s future performance, this process allows the students to rigorously benchmark the performance of alternative techniques on historical data. It not only identifies the empirical ex-post leader, but also highlights how much more accurate or less accurate other methods were so they can better understand the practical value and predictive limitations of different approaches. This in-depth workflow conveys the types of analysis real-world data scientists and business analysts would carry out to select the optimal forecasting technique.

CAN YOU PROVIDE MORE DETAILS ON HOW THE PROPOSED MODEL WOULD ASSESS COMPETENCIES AND LEARNING OUTCOMES?

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The proposed model aims to provide a comprehensive and multifaceted approach to assessing competencies and learning outcomes through both formative and summative methods. Formatively, students would receive ongoing feedback throughout their learning experience to help identify areas of strength and areas needing improvement. Summatively, assessments would evaluate the level of competency achieved at important milestones.

Formative assessments could include techniques like self-assessments, peer assessments, and process assessments conducted by instructors. Self-assessments would ask students to periodically reflect on and rate their own progress on various dimensions of each target competency. Peer assessments would involve students providing feedback to one another on collaborative work or competency demonstrations. Process assessments by instructors could include observations of student performances in class with rubric-based feedback on skills displayed.

Formative assessments would not be high-stakes evaluations but rather be geared towards guidance and improvement. Feedback from self, peer, and instructor sources would be compiled routinely in an individualized competency development plan for each student. This plan would chart progress over time and highlight areas still requiring focus. Instructors could then tailor learning activities, projects, or supplemental instruction accordingly to best support competency growth.

Summative assessments would serve to benchmark achievement at key transition points. For example, capstone courses at the end of degree programs could entail comprehensive competency demonstrations and evaluations. These demonstrations might take the form of student portfolios containing samples of their best work mapped to the targeted outcomes. Students could also participate in simulations, case studies, or practicum experiences closely mirroring real-world scenarios in their fields.

Evaluators for summative assessments would utilize detailed rubrics to rate student performances across multiple dimensions of each competency. Rubrics would contain clear criteria and gradations of competency level: exemplary, proficient, developing, or beginning. Evaluators would consider all available evidence from the student’s learning experience and aims to achieve inter-rater reliability. Students would receive individualized scored reports indicating strengths and any remaining gaps requiring remediation.

Assessment results would be aggregated both at the individual student level as well as at the program level, disaggregated by factors like gender, race, or academic exposure. This aggregation allows identification of systemic issues or biases benefiting from program improvements. It also permits benchmarking against outcomes at peer institutions. Student learning outcomes and competency achievements could be dynamically updated based on this ongoing review process.

For competencies spanning multiple levels of complexity, layered assessments may measure attainment of basic, intermediate and advanced levels over the course of a degree. As students gain experience and sophisticated in their fields, evaluations would shift focus to higher orders of application, synthesis, and creativity. Mastery of advanced competencies may also incorporate components like student teaching, research contributions, or externship performance reviews by employers.

Upon degree completion, graduates could undertake capstone exams, licensure/certification exams, or portfolio reviews mapped to the final programmatic competency framework. This would provide a final verification of readiness to perform independently at entry-level standards in their disciplines. It would also allow ongoing refinement and alignment of curriculum to ensure graduation of competent, career-ready professionals.

By utilizing a blended learning model of varied formative and summative assessments, mapped to clearly defined competencies, this proposed framework offers a comprehensive, evidence-based approach to evaluating student learning outcomes. Its multi-rater feedback and emphasis on competency growth over time also address critiques of high-stakes testing. When implemented with rigor and ongoing review, it could help ensure postsecondary education meaningfully prepares graduates for their careers and lifelong learning.

CAN YOU PROVIDE SOME EXAMPLES OF KAGGLE COMPETITIONS THAT WOULD BE SUITABLE FOR BEGINNERS

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Titanic: Machine Learning from Disaster (Beginner-friendly): This is widely considered the best competition for newcomers to Kaggle as it is straightforward and a classic “getting started” type of problem. The goal is to predict which passengers survived the sinking of the RMS Titanic using variables like age, sex and passenger class. This was one of the earliest competitions on Kaggle and has a very clear objective. Cleaning and exploring the data is quite simple, and many common machine learning algorithms like logistic regression, decision trees, and random forests can be applied. This competition introduces the basic pattern of exploring data, building models, and submitting your predictions for evaluation.

Digit Recognizer: This competition asks Kagglers to predict the digit that appears in images of handwritten digits from 0-9. The data contains thousands of 28×28 pixel greyscale images of handwritten single digits. This competition has simple, pre-processed data and a clear classification task, making it good for beginners. Common techniques like convolutional neural networks (CNNs) have proven very effective. While computer vision problems can require more advanced techniques, the data preparation and model building is quite straightforward here.

House Prices – Advanced Regression Techniques: The goal here is to predict housing prices using a provided historical dataset from Ames, Iowa. The features include basic housing information like sqft living, the number of bedrooms, year built etc. This dataset lends itself well to introductory regression techniques like linear regression, gradient boosting and random forest regression. The objective and features are clearly defined. Cleaning and exploring the data involves standard approaches to numeric and categorical variables. This competition allows newcomers to learn common regression techniques before tackling more complex data types.

Bike Sharing Demand: This competition uses historical hourly and seasonal data from the Capital Bikeshare bike rental program in Washington D.C. to predict future bike rental demand. Predictors include weather, dates and times. Forecasting problems are very common in machine learning and this represents an straightforward introduction to the genre with its clear objective and numeric features. Again, common regression algorithms like gradient boosting and XGBoost can be effectively applied. Feature engineering ideas like handling datetimes and including previous rentals as predictors can be explored. The core techniques are entry-level but introduce a relevant business problem.

SIIM-ACR Pneumothorax Segmentation: This medical imaging competition introduces computer vision concepts while still being relatively appropriate for beginners. The task involves segmenting regions of potential pneumothorax (collapsed lung) within X-ray images. While computer vision modeling, especially with deep learning, can get quite advanced, basic convolutional or encoder-decoder type models have achieved good results on this dataset. Similarly to the Digit Recognizer challenge, the data is pre-processed and the classification objective is clear. Common frameworks like Keras and PyTorch allow fast model building and experimentation to learn foundational CV methods. The real-world medical application also provides strong motivation for newcomers.

These Kaggle competitions provide clear, self-contained problems well-suited to explore foundational machine learning techniques. They introduce standard algorithm types, common data wrangling tasks, and validation strategies in realistic and relevant prediction scenarios. The digit, housing, rental demand, medical imaging examples can each be effectively tackled by applying logistic regression, linear regression, random forest, boosting, or CNN models – algorithms appropriate for new learners. The clean Titanic and housing datasets make data exploration straightforward. These competitions allow beginners to start developing machine learning skills through exposure to varied techniques and domains, while keeping modeling itself approachable. They set the stage for exploring increasingly complex problems as skills progress.

CAN YOU PROVIDE AN EXAMPLE OF HOW THE PREDICTED DEMAND HEATMAPS WOULD LOOK LIKE

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Predicted demand heatmaps are visualizations that ride-hailing companies like Uber and Lyft generate to forecast where and when passenger demand for rides will be highest. These heatmaps are produced using machine learning algorithms that analyze vast amounts of past ride data to identify patterns and trends. They are intended to help the companies optimize driver supply to meet fluctuations in rider demand across cities over time.

Some key factors that are typically used to generate these predictive heatmaps include: date, day of week, time of day, holidays/events, weather patterns, traffic conditions, densities of points of interest like restaurants/bars, public transportation schedules, demographic data on populations and their commuting/travel habits. The machine learning models are constantly being retrained as new ride data becomes available, improving their forecasting accuracy over time.

For example, let’s look at what a predictive demand heatmap for a major city like New York City may look like on a typical Friday evening. We’ll focus on the 5pm to 8pm time period. At 5pm, the model would predict moderate demand across much of Manhattan as people finish work and start to head home or to happy hour spots. Demand would be somewhat concentrated around transit hubs like Grand Central and Penn Station as commuters enter the city.

Moving to 6pm, demand increases notably in the midtown and downtown areas as after-work socializing and dining out picks up steam. Popular entertainment and nightlife zones like the East and West villages would show strong demand hotspots. Commuter-centric pockets near transit become less prominent as rush hour disperses. Outlying boroughs like Brooklyn and Queens would exhibit growing but still modest demand levels.

By 7pm, Manhattan demand swells considerably, with very high-intensity hotspots dotting the map around prime dinner and bar neighborhoods. Moneyed areas like the Upper East Side, Chelsea and SoHo glow bright red. Streets surrounding Madison Square Garden or Broadway theaters flare up on event nights. Uptown zones near Central Park see less dramatic but steadier increases. Brooklyn Heights, Williamsburg and LIC emerge as outer-borough hotspots too.

At the 8pm mark, Manhattan demand reaches its peak intensity for the evening across a wide geography. Only the far Upper West and Upper East sides remain more tempered. Public transit stations show intense “bulges” as evening commuter flows build up again. Downtown Brooklyn and parts of western Queens pick up substantially as well. By contrast, outer areas like Staten Island or The Bronx exhibit only pockets of light demand at this hour on a typical Friday.

Of course, this is just one example using generic patterns – the actual predictive heatmaps factor in real-time adjustments for live events, construction, weather extremes or other unplanned variations that can influence travel behaviors. But it illustrates the type of spatial and temporal demand evolution ridesharing platforms aim to model across cities worldwide. These forecasting tools empower companies to strategically position available drivers and proactively handle surges, improving both efficiency and customer satisfaction over time.

While predictive analytics continue advancing, uncertainties will always exist when projecting human mobility behaviors. But democratizing urban transportation requires understanding fluctuating demand at a hyperlocal scale. Machine learning-enabled heatmaps represent an innovative approach towards optimally matching dynamic rider needs with dynamic driver supplies. As more ride data flows in, these predictive mapping technologies should grow ever more precise – helping riders easily get a ride, while helping drivers easily find their next fare.

Predictive demand heatmaps leverage powerful analytics to visualize expected usage hotspots for ride-hailing networks across cities and moments in time. They aim to optimize the passenger experience and driver utilization through data-driven operations. As an emerging application of artificial intelligence in transportation, their full potential to efficiently connect urban mobility supply and demand has yet to be fully realized. But with ongoing enhancement, these forecasting tools could meaningfully impact how people navigate and experience metropolitan regions worldwide every day.