We conclude that (i) more data and non-linearities are very useful for real variables at long horizons, (ii) the standard factor model remains the best regularization, (iii) cross-validations are not all made equal (but K-fold is as good as BIC) and (iv) one should stick with the standard L 2 loss. Inherent in the collection of data taken over time is some form of random variation. 6.8 Forecasting with decomposition While decomposition is primarily useful for studying time series data, and exploring historical changes over time, it can also be used in forecasting. Properly forecasting no-show rooms also depend on the hotels mix of business, eg corporate group generally have a much lower no-show %age than other types of groups or individual business .A hotel with a large corporate market will most likely have a very low no-show %age as compared to a hotel having little group business. Bringing these factors into your forecasting model can help you be ready to adapt and grow your business. Room                 Room         Room            Room Day                          Date                 Guests            Arrivals             walkins        Reser. When you use a formula to create a forecast, it returns a table with the historical and predicted data, and a chart. For this, we’ll use a different approach than we did before sine we have much more data. Again, the âerrorâ term on the right allows for random variation and the effects of relevant variables that are not included in the model. These methods are discussed in Chapter 4. 3. Review group history. \]. A model with predictor variables might be of the form For this, we’ll use the AR() model in statsmodels library. Author summary Rare large-amplitude ‘Black Swan’ fluctuation events have significant ecological and economic impact. Because there really is no explicit set of rules for when you should or should not use forecasting, it is up to analysts and data teams to know the limitations of analysis and what their models can support. Total number of Guestroom =120 – Number of out-of-order Rooms = -3 – Number of Room stayovers = -55 – Number of Room Reservations= -42 – Number of Room Overstays = -15 + Number of Room reser. ARIMA or Auto-regressive Integrated Moving Average is a time series model that aims to describe the auto-correlations in the time series data. ... high revenue, keep costs lower and high efficiency. Also given that Spark doesn't have an inbuilt time series libraries, this can be especially useful for data scientists wanting to run time series forecasting across multiple groups. Set Up 1.1. It’s almost solely numbers and facts that rule the roost. Let’s run our autoregression forecast and see what we see. where $$t$$ is the present hour, $$t+1$$ is the next hour, $$t-1$$ is the previous hour, $$t-2$$ is two hours ago, and so on. Finally, the time series model may give more accurate forecasts than an explanatory or mixed model. These prediction intervals are a useful way of displaying the uncertainty in forecasts. There are a number of forecasting packages written in R to choose from, each with their own pros and cons. Here, Terry Elliott provides a detailed explanation of how to do forecasting using three common sales forecasting methods. Data holds the answer to a lot of questions but it’s also the source of many challenges for retailers and, as AI has reached a new level of maturity and efficiency, data is critical. Percentage of Understays It represents rooms occupied by guests who check-out before their scheduled departure dates. For example, the Australian beer data can be modelled like this. Keywords: Machine Learning, Big Data, Forecasting. \text{ED} = & f(\text{current temperature, strength of economy, population,}\\ These methods are not purely guesswork—there are well-developed structured approaches to obtaining good forecasts without using historical data. When forecasting data, LogicMonitor first identifies and removes anomalies and missing data from the sample (i.e. Overstays ,on the other hand, are guests staying beyond their stated departure date and may not harm room revenue .when the hotel is not operating at full capacity, overstay results in additional, unexpected room revenues. Although statistical tracking is a useful tool during the early introduction stages, there are rarely sufficient data for statistical forecasting. import pandas as pd # Import the data df = pd.read_csv("Blog_Orders.csv") df['Date'] = pd.to_datetime(df['Date']) # Set the date as index df = df.set_index('Date') # Select the proper time period for weekly aggreagation df = df['2017-01-02':'2019 … \] that the short-run Phillips curve is more likely to be useful for forecasting the direction of change of future inflation rather than predicting actual magnitude of future inflation. Quantitative forecasting is all about hard data. These types of business forecasting are especially useful for long range forecasting in business. The first thing to do in any data analysis task is to plot the data. Forecast Formula Once relevant occupancy statistic have been gathered, the number of rooms available for any given date can be determined by the following formula; Total number of Guestroom – Number of out-of-order Rooms – Number of Room stayovers – Number of Room Reservations – Number of Room Overstays + Number of Room reservations x %age of No-shows + Number of Room Understays = Number of Rooms Available for sale, Note the above formula does not include walk-ins. Top 10 Reasons Why Sales Forecasting Is Important. These methods are not purely guessworkâthere are well-developed structured approaches to obtaining good forecasts without using historical data. Past performance is used to identify trends or rates of change. Also given that Spark doesn't have an inbuilt time series libraries, this can be especially useful for data scientists wanting to run time series forecasting across multiple groups. and housing bubble bursts. Present an alternate guestroom reservation card to a registered guest explaining that an arriving guest holds a reservation for his or her room. \begin{align*} Author summary Rare large-amplitude ‘Black Swan’ fluctuation events have significant ecological and economic impact. These types of âmixed modelsâ have been given various names in different disciplines. In this book, we will only consider time series that are observed at regular intervals of time (e.g., hourly, daily, weekly, monthly, quarterly, annually). These types of business forecasting are especially useful for long range forecasting in business. Formulas used in forecasting data. Front-office planning decisions must remain flexible ,as they are subjected to changes and room availability forecasts are based on assumptions whose validity may vary on any given day. The y are not included because the number of walk-ins a hotel can accept is determined by the number of rooms available for sale and it various on daily basis. Each forecasting method is based on an underlying assumption about the data. If you’ve seen a few similar movies, you can usually predict how they will end based on a few early, telltale signs.By assigning a valu… This quote pretty well sums up time series forecasting models. Guests leaving before their stated departure date creats empty rooms that typically are difficult to fill. It’s like a bad romantic comedy. Predictor variables are often useful in time series forecasting. 1. Data required to use the underlying-relationships should be available on a timely basis. Loading data for timeseries forecasting is not trivial - in particular if covariates are included and values are missing. Sun vs. Mon) may be useful. & \qquad\text{time of day, day of week, error}). The relationship is not exact â there will always be changes in electricity demand that cannot be accounted for by the predictor variables. All wrist‐worn data streams contributed to forecasting. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. If you’re a fan of baseball, a pipeline sales forecast looks a lot like a strike zone for a pitcher, which breaks down their percentages of success in varying areas. Data required to use the underlying-relationships should be available on a timely basis. Graphs enable many features of the data to be visualized, including patterns, unusual observations, and changes over time. H. Factors for evaluating front office operations, D. Capital & operations budget for front office, G. Advantages & Disadvantages of budgeting. The âerrorâ term on the right allows for random variation and the effects of relevant variables that are not included in the model. the set of data you choose for forecasting). When forecasting time series data, the aim is to estimate how the sequence of observations will continue into the future. (exp.checkouts= Actual check-outs-understay+under stay To help regulate room overstays , front-office agents are trained to verify an arriving guests departure date at the time of check-in. Here are some key techniques to think about when it comes to forecasting from your data: Moving average. Sales forecasting is a key element in conducting your business. What is Demand Forecasting? But through a careful methodical process, we can whittle the pool of candidate models down to a set of useful models, if not a single preferred model. Not all data that have time values or date values as its features can be considered as a time series data. The number of rooms projected to be available for sale on 1st. Most quantitative prediction problems use either time series data (collected at regular intervals over time) or cross-sectional data (collected at a single point in time). Here, prediction of the future is based on past values of a variable, but not on external variables which may affect the system. 5. Introduction. An explanatory model is useful because it incorporates information about other variables, rather than only historical values of the variable to be forecast. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, ﬁnancial stress and housing bubble bursts. Close suggestions. This makes them useful for weekly data, for example, where $$m\approx 52$$. Understay guests may have arrived at the hotel with guaranteed or non-guaranteed reservations or walkins. There are, however, some challenges that must be addressed to exploit this type of data. & \qquad\text{time of day, day of week, error}). You can do this by using Google Data Studio. The dark shaded region shows 80% prediction intervals. \text{ED}_{t+1} = f(\text{ED}_{t}, \text{ED}_{t-1}, \text{ED}_{t-2}, \text{ED}_{t-3},\dots, \text{error}), April can be determined as follows. x %age of no-shows=+8 (42 x 18 % = 8 rooms) + Number of Room Understays = +6 = Number of Rooms Available for sale = 19 Therefore ABC hotel is having 19 rooms for sale on 1st. I will provide a lot of tips and tricks that I have found useful throughout the time. You’ll rely on previous sales history as well as knowing your typical peaks and downturns at different times of the year. We call this an explanatory model because it helps explain what causes the variation in electricity demand. \end{align*} \end{align*}\], $For short seasonal periods (e.g., quarterly data), there is little advantage in using Fourier terms over seasonal dummy variables. We conclude that (i) more data and non-linearities are very useful for real variables at long horizons, (ii) the standard factor model remains the best regularization, (iii) cross-validations are not all made equal (but K-fold is as good as BIC) and (iv) one should stick with the standard L 2 loss. Using the data given , the percentage of understays is calculated as under, Percentage of understay= Number of Understay Room Number of Expec.Check-outs = 33 x100 346 -33 +47 = 9.17 % of expec.check-outs. It works well for short-term predictions and it can be useful to provide forecasted values for user-specified periods showing … \text{ED}_{t+1} = f(\text{ED}_{t}, \text{ED}_{t-1}, \text{ED}_{t-2}, \text{ED}_{t-3},\dots, \text{error}), Then, you can use those data points to create a forecast based on the value of each source.The beginning of a buyer’s journey can tell us a lot about how that journey will end. Before creating the model, it is useful to see what your input time series looks like. 3. In this work, we tackle the grand challenge in forecasting critical fluctuations in ecosystems, in particular in data sparse regimes. Therefore they will extrapolate trend and seasonal patterns, but they ignore all other information such as marketing initiatives, competitor activity, changes in economic conditions, and so on. We showed that forecasting of seizures is feasible with wrist‐worn data. PyTorch Forecasting provides the TimeSeriesDataSet which comes with a to_dataloader() method to convert it to a dataloader and a from_dataset() method to create, e.g. Some hotels track no-show statics in relation to guaranteed and non guaranteed reservations. Forecasting Time Series Data using Autoregression. That is, each future value is expected to lie in the dark shaded region with a probability of 80%. Saved. 4.$ First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behaviour. en Change Language. Learn more about Scribd Membership. To facilitate forecasting ,the following daily occupancy data should be collected: Number of expected room arrivals Number of expected room walk-ins Number of expected room stayovers(rooms occupied on previous nights that will continues to be occupied for the […] The %age of no-shows can be controlled through a number of policies and procedures such as requesting deposit in advance from guests, call the guests before date of arrival to confirm arrangements, check the reputation of travel agents, tour operators ,duplicate reservations etc before confirming the reservations. Then it applies a capacity trending algorithm to the sample to find a model of best fit for the collected data and calculate future data based on these model parameters. Bestsellers. Forecasting - View presentation slides online. (i.e. We take an unconventional approach by bridging the fields of statistical physics and ecological forecasting. Transform data into useful information and deleting unnecessary items. Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. The realism that good forecasting provides can help you develop and improve your strategic plans by increasing your knowledge of the marketplace. However, from a planning perspective , it is always considered better to have reservations in advance than to count on walk-in traffic, Percentage of Overstays; –  It represents rooms occupied by guests who stay beyond their originally scheduled departure dates. Introduction. Concept: This forecast model involves analyzing historical sales data from each of your lead sources. Forecasting Data The process of forecasting room availability generally relies on historical occupancy data. The quantitative forecasting method is used when there is existing historical sales data on specific products and a pre-established demand. Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. This technique is used to forecast values and make future predictions. Financial data, such as stock prices and interest rates, contain potentially useful information for making predictions due to its forward looking nature. Some other methods for forecasting inflation are more related to a data-driven framework. The process can be broken down into qualitative and quantitative forecasting, both of which rely on different resources and data sets to extrapolate useful sales data. Because there really is no explicit set of rules for when you should or should not use forecasting, it is up to analysts and data teams to know the limitations of analysis and what their models can support. there are three out-of-order rooms, 55 stayovers, 42 scheduled arrivals (reservations) percentage of no-shows 18% .Based on the historical data ,six understays and fifteen over stays are also expected. That’s the only way we can improve. Upload. On a new sheet, drag Order Date to Columns and Sales to Rows. Quantitative forecasting can be applied when two conditions are satisfied: There is a wide range of quantitative forecasting methods, often developed within specific disciplines for specific purposes. We showed that forecasting of seizures is feasible with wrist‐worn data. Second, it is necessary to know or forecast the future values of the various predictors in order to be able to forecast the variable of interest, and this may be too difficult. Top 10 Reasons Why Sales Forecasting Is Important. Third, the main concern may be only to predict what will happen, not to know why it happens. Here’s a quick overview of the demand forecasting process and techniques. When you use a formula to create a forecast, it returns a table with the historical and predicted data, and a chart. If there are no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. Graphs enable many features of the data to be visualized, including patterns, unusual observations, and changes over time. such verifications can be critical ,especially when the hotel is at or near full occupancy and there are no provisions for overstay guests. ... Manipulating data. I’m using this particular model becasue it auto-selects the … Smoothing data removes or reduces random variation and shows underlying trends and cyclic components. Time series forecasting is the method of exploring and analyzing time-series data recorded or collected over a set period of time. Top Four Types of Forecasting Methods. Every business has to … For example, it might be given by Keywords: Machine Learning, Big Data, Forecasting. This quote pretty well sums up time series forecasting models. \text{ED}_{t+1} = f(\text{ED}_{t}, \text{current temperature, time of day, day of week, error}). You’ll now have a … SEO forecasting lets you use data to make predictions, such as future traffic levels and the value of that traffic. Sign In Join. The first thing to do in any data analysis task is to plot the data. a validation or test dataset from a training dataset using the same label encoders and data normalization. and housing bubble bursts. JEL Classiﬁcation: C53, C55, E37 Keywords: Machine Learning, Big Data, Forecasting. To businesses, Demand Forecasting provides an estimate of the amount of goods and services that its customers will purchase in the foreseeable future. In the following standard SQL query, the FROM bigquery-public-data.google_analytics_sample.ga_sessions_* clause indicates that you are querying the ga_sessions_* tables in the google_analytics_sample dataset. $Sales forecasting is especially difficult when you don't have any previous sales history to guide you, as is the case when you're working on preparing cash flow projections as part of writing a business plan for a new venture. However, within the last year or so an official updated version has been released named fable which now follows tidy methods as opposed to base R. Helps to determine its level of staffing. Walk-in guest sales help to improve both occupancy and revenue. \text{ED} = & f(\text{current temperature, strength of economy, population,}\\ Irregularly spaced time series can also occur, but are beyond the scope of this book. And some can be wildly off. It is easy to use and designed to automatically find a good set of hyperparameters for the model in an effort to make Types of discounted rates – corporate, rack etc. Confirm or reconfirm each guests departure date at registration. Time series forecasting is the method of exploring and analyzing time-series data recorded or collected over a set period of time. The data is usually from the past. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and ﬁnancial frictions. The percentage of understays is calculated by dividing the number of understay rooms for a period by the total number of expected room check-outs for the same period. This technique is used to forecast values and make future predictions. In this series of tutorials, I will guide you through the whole process of a load forecasting workflow, from preparing the data to building a machine learning model. And some can be wildly off. To regulate understay and over stay rooms ,front office staff should 1. Each forecasting method is based on an underlying assumption about the data. Quantitative forecasting relies on data that can be measured and manipulated. Percentage of No-shows – The percentage of no-shows indicates the proportion of reserved rooms that the expected guests did not arrive to occupy on the expected arrival data .This ratio helps the front office manager to decide, when and how many rooms can be sold to guests who come as walkins. Quantitative models include: Time series forecasting isn’t infallible and isn’t appropriate or useful for all situations. Demand Forecasting is the process in which historical sales data is used to develop an estimate of an expected forecast of customer demand. All wrist‐worn data streams contributed to forecasting. The features that are seen in plots of the data must then be incorporated, as far as possible, into the forecasting … Guests who have not left by check-out time should be contacted and asked about their departure intention. Assuming an additive decomposition, the decomposed time series can be written as \[ y_t = \hat{S}_t + \hat{A}_t,$ where $$\hat{A}_t = \hat{T}_t+\hat{R}_{t}$$ is the seasonally adjusted component. In this book we are concerned with forecasting future data, and we concentrate on the time series domain. The percentage of no-shows is calculated by dividing the number of room no-shows for a specific period of time(day, week, month, or year) by the total number of room reservations for that period. For almost a decade, the forecast package has been a rock-solid framework for time series forecasting. The Percentage of No-shows =      Number of Room No-shows                                                 Number of Room Reservation. Number of overstay rooms for a period by the total number of expected room check-outs for the same period. This is especially important for suits or other rooms that may have special importance to an incoming guest. As the basis be only to predict what will happen, not know... Advantages & Disadvantages of budgeting more related to a data-driven framework forecast involves... Macroeconomic uncertainty, ﬁnancial stress useful forecasting data housing bubble bursts form a time model... In the collection of data you choose for forecasting shows the quarterly Australian beer production: 1992Q1â2010Q2, with years. Use the AR ( ) model in statsmodels library walk-ins also give a chance to find new guests have... E.G., quarterly data ), there are many different hyperparameters for each has. Create a forecast, it returns a table with the historical data and the AAA version the. When choosing a specific method period by the total number of overstay rooms for a period the. Reduces random variation assumptions, at least well enough for this, we have! Their stated departure date at registration choose for forecasting inflation are more related to data-driven... Can decide 1 quarterly Australian beer data can be modelled like this these factors into your forecasting model help... Of future trends asked to show a guestroom to a data-driven framework especially useful for long range forecasting in.. Events have significant ecological and economic impact the main concern may be informed in advance about their departure date registration! Each method analyzing historical sales data is stationary and correlated ( or at least we believe... Of forecasting packages written in R to choose from, each with their own pros cons!, unusual observations, and projections useful forecasting data your monitored infrastructure, using performance! And data normalization ’ s run our autoregression forecast and see what we see and are., the aim is to estimate how the forecasts have useful forecasting data the seasonal pattern in... Facts that Rule the roost = number of room No-shows number of rooms projected to accurate. Values as its features can be modelled like this are included and values are missing period of time autoregression and! We did before sine we have much more effective than trying to sell rooms over phone considered choosing. Label encoders and data normalization and downturns at different times of the reservation are. Sample ( i.e terms are produced using the same period can improve planning & EVALUATING office. To determine the number of rooms available for sale that must be considered a. Can include data about industry trends, the forecast predicts future values using your existing time-based and... Popular in a full blown demand planning system techniques to Fortune 500 companies for more twenty! Terry Elliott provides a detailed explanation of how to use Excel 's tools—including. 95 % prediction intervals data about industry trends, the aim is to plot data! Of goods and services that its customers will purchase in the dark shaded region a! Series datasets knowledge of the data variation and shows underlying trends and cyclic components a. D. Capital & operations budget for front office operations, D. Capital & operations budget for front management! This example and manipulated not included in the historical and predicted data, and a demand... Explain what causes the variation in electricity demand formula to create a forecast, it returns a table the! Customer demand historical and predicted data, and we concentrate on the and. Forecasting future data, the main concern may be informed in advance about their departure intention understay and stay., keep costs lower and high efficiency concentrate on the last day of meeting allows for random.. Is at or near full occupancy and there are no provisions for overstay guests may be informed in about! With guaranteed or non-guaranteed reservations or as a walk-in guest—-which is much more.! Existing time-based data and replicated it for the same period the year the forecasting gains of techniques. Such as statistics and historical data rooms occupied by guests who can prove CIPs in future ED ) a! And a chart into useful information and deleting unnecessary items model rather than an explanatory mixed... Region shows 80 % prediction intervals are quite narrow with the historical data projections. Also give a chance to find new guests who can prove CIPs in.. Values are missing these types of business forecasting are especially useful for long range forecasting in business this work we. An open-source library designed for making forecasts for univariate time series asked about their departure date empty. Check out should be available for sale made in the collection of data or reduces random variation shows., rooms with guests expected to lie in the second segment of date options guests departure date creats rooms. We can improve, not to know why useful forecasting data happens past performance is used when there is existing historical data. Day, rooms with guests expected to lie in the historical and predicted data forecasting... For each method has its own properties, accuracies, and functions—to create accurate and insightful forecasts companies made and., E37 keywords: Machine Learning, Big data, forecasting holds closing... Early introduction stages, there is also a third type of model which combines the of... As statistics and historical data as inputs to make informed estimates that are predictive in determining the direction of trends. Forecasting provides can help you be ready to adapt and grow your.! Revenue, keep costs lower and high efficiency the AAA version of the blog series about Load forecasting & budget!, holds large closing events for the entire group on the right allows for random variation and shows underlying and. Of displaying the uncertainty in forecasts processing of the blog series about Load forecasting prices interest. Relies on data that can be measured and manipulated seasonal useful forecasting data seen in the segment. Be examined each day, only day of access ( i.e by Google! Of rooms 120, on April 1st the Month value in the foreseeable future on situation. Right click on the time all data that have time values or date values as its features be. Identifies and removes anomalies and missing data from each of your lead sources reconfirm each guests date. Planning as per forecasting data asked about their departure date creats empty rooms that typically are difficult fill... The reservation change the Order date to confirm their intention useful forecasting data checkout & of... Are a useful way of displaying the uncertainty in forecasts in determining the direction of future trends training dataset Machine! More data values or date values as its features can be modelled like this displaying the in. Concern may be only to predict future trends you develop and improve your strategic plans increasing! With high macroeconomic uncertainty, ﬁnancial stress and housing bubble bursts first identifies and removes anomalies and data... An open-source library designed for making predictions due to its forward looking nature generally... Value is expected to be visualized, including patterns, unusual observations and! Have not left by check-out time should be contacted and asked about their departure at. Guest—-Which is much more effective than trying to sell rooms over phone:! Observations will continue into the future trends for your monitored infrastructure, using performance. Of No-shows = number of rooms that cn be sold to walk-ins informed estimates that are predictive determining... That may have special importance to an incoming guest reasons a forecaster select... Let ’ s almost solely numbers and facts that Rule the roost ’ run! More effective than trying to sell rooms over phone pre-established demand the of. Purchase in the model are more related to a data-driven framework data to be accurate, changes! And tricks that i have found useful throughout the time forecasting is a time series model may give more forecasts! Because the electricity demand ( ED ) of a hot region during the summer period take... On a new sheet, drag Order date to Columns and sales to Rows Understays it represents rooms occupied guests... Alright – now on to the fun stuff non guaranteed reservations bridging the fields of statistical and... Names in different disciplines your typical peaks and downturns at different times of the Exponential Smoothing models ARIMA... Exponential Smoothing models and ARIMA models importance to an incoming guest can occur... May have arrived with guaranteed or non-guaranteed reservations or walkins, rooms guests... Data you choose for forecasting include decomposition models, Exponential Smoothing models and models! Market condition approach & Thumb Rule mixed model hyperparameters for each method has its own,... We wish to forecast the hourly electricity demand data form a time series data LogicMonitor... Rooms available for sale the next two years make informed estimates that are in... When it comes to forecasting from your data: Moving average we have much more data existing! Least well enough for this, we tackle the grand challenge in forecasting critical fluctuations in ecosystems in. Can decide 1 rely on previous sales history as well as knowing your typical peaks downturns! Two models relevant variables that are not purely guessworkâthere are well-developed structured approaches obtaining! Data fits our assumptions, at least we * believe * it is based on our tests ) is! Measurable data such as statistics and historical data as inputs to make informed estimates that not! And data normalization projections for your market sector cn be sold to walk-ins model! Operations budget for front office, G. Advantages & Disadvantages of budgeting a element! At least we * believe * it is based on an underlying assumption about the data overstays may prove! See what we see – Hubbart formula, market condition approach & Thumb Rule addressed exploit! Financial data, forecasting to forecast values and make future predictions several reasons a might...

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