{"id":676,"date":"2020-12-10T14:55:45","date_gmt":"2020-12-10T14:55:45","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/12\/10\/amazon-forecast-weather-index-automatically-include-local-weather-to-increase-your-forecasting-model-accuracy\/"},"modified":"2020-12-10T14:55:45","modified_gmt":"2020-12-10T14:55:45","slug":"amazon-forecast-weather-index-automatically-include-local-weather-to-increase-your-forecasting-model-accuracy","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/12\/10\/amazon-forecast-weather-index-automatically-include-local-weather-to-increase-your-forecasting-model-accuracy\/","title":{"rendered":"Amazon Forecast Weather Index \u2013 automatically include local weather to increase your forecasting model accuracy"},"content":{"rendered":"
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We\u2019re excited to announce the Amazon Forecast Weather Index, which can increase your forecasting accuracy by automatically including local weather information in your demand forecasts with one click and at no extra cost. Weather conditions influence consumer demand patterns, product merchandizing decisions, staffing requirements, and energy consumption needs. However, acquiring, cleaning, and effectively using live weather information for demand forecasting is challenging and requires ongoing maintenance. With this launch, you can now include 14-day weather forecasts for US and Europe locations with one click to your demand forecasts.<\/p>\n

The Amazon Forecast Weather Index combines multiple weather metrics from historical weather events and current forecasts at a given location to increase your demand forecast model accuracy. Amazon Forecast<\/a> uses machine learning (ML) to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at Amazon.com to developers as a fully managed service, removing the need to manage resources or rebuild your systems.<\/p>\n

Changes in local weather conditions can impact short-term demand for products and services at particular locations for many organizations in retail, hospitality, travel, entertainment, insurance, and energy domains. Although historical demand patterns show seasonal demand, advance planning for day-to-day variation is harder.<\/p>\n

In retail inventory management use cases, day-to-day weather variation impacts foot traffic and product mix. Typical demand forecasting systems don\u2019t take expected weather conditions into account, leading to stock-outs or excess inventory at some locations, resulting in the need to transfer inventory mid-week. For example, for retailers, knowing that a heat wave is expected, they may choose to over-stock air conditioners from distribution centers to specific store locations. Or they may choose to prepare different types of grab-and-go prepared food items depending on the weather conditions.<\/p>\n

Outside of product demand, weather conditions also impact staffing needs. For example, restaurants can better balance staff dependent on dine-in vs. take-out orders, or businesses with warehouses can better predict the number of workers that may come into work because of disrupted transportation. Although store managers may be able to make one-off stocking decisions based on weather conditions using their intuition and judgment, making buying, inventory placement, and workforce management decisions at scale becomes more challenging.<\/p>\n

Day-to-day weather variation also impacts hyper-local on-demand services that rely on efficient matching of supply and demand at scale. A looming storm can lead to high demand for local ride hailing or food delivery services, while also impacting the number of drivers available. Having the information of upcoming weather changes enables you to better meet customer demand. Programmatically applying local weather information at scale can help you preemptively match supply and demand.<\/p>\n

Predicting future weather conditions is common, and although it\u2019s possible to use these predictions to more accurately forecast demand for products and services, it may be a struggle to do so in practice. Acquiring your own historical weather data and weather forecasts is expensive, and requires constant data collation, aggregation, and cleaning. Additionally, without weather domain expertise, transforming raw weather metrics into predictive data is challenging.<\/p>\n

With today\u2019s launch, you can account for local day-to-day weather changes to better predict demand, with only one click and at no additional cost, using Forecast. When you use the Weather Index, Forecast trains a model with historical weather information for the locations of your operations and uses the latest 14-day weather forecasts on items that are influenced by day-to-day variations to create more accurate demand forecasts.<\/p>\n

Tom Summerfield is the Director of Retail at Peak.AI<\/a>, an accessible AI system that harnesses the power of data to assist\u2014not displace\u2014humans to improve business efficiency and productivity. Summerfield says, \u201cAt Peak, we work with retail, CPG, and manufacturing customers who all know that weather plays a strong role in dictating consumer buying habits. Variation in weather ultimately impacts their product demand and product basket mix. Our customers frequently ask us to include weather in their demand forecasts. With Amazon Forecast adding a weather feature, we are now able to seamlessly integrate these insights and improve the accuracy of our demand planning models.\u201d<\/p>\n

The Weather Index is currently optimized for in-store retail demand planning and local on-demand services, but may still add value to scenarios where weather impacts demand such as power and utilities. As of this writing, the Weather Index is only available for US and Europe Regions. Other Regions will become available soon. For more information about latitude-longitude bounding boxes and US zip codes supported, see Weather Index<\/a>.<\/p>\n

Using the Weather Index for your forecasting use case<\/h2>\n

You can add local weather information to your model by adding the Weather Index during training. In this section, we walk through the steps to use the Weather Index on the Forecast console. For this post, we use the New York City Taxi dataset<\/a>. To review the steps through the APIs, refer to the following notebook in our GitHub repo<\/a>, where we have a cleaned version of the New York Taxi dataset ready to be used.<\/p>\n

The New York Taxi dataset has 260 locations and is being used to predict the demand for taxis per location per hour for the next 7 days (168 hours).<\/p>\n

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  1. On the Forecast console, create a dataset group.<\/li>\n<\/ol>\n

    \"On<\/p>\n

      \n
    1. Upload the historical demand dataset as the target time series. This dataset must include geolocation information for you to use the Weather Index.<\/li>\n
    2. Select Schema builder<\/strong>.<\/li>\n
    3. Choose your location format (for this post, the dataset includes latitude and longitude coordinates).\u00a0<\/em>\n<\/li>\n<\/ol>\n

      Forecast also supports postal codes for US only.<\/p>\n

        \n
      1. For Dataset import details<\/strong>, select Select time zone<\/strong>.<\/li>\n
      2. Choose your time zone (for this post, we choose America\/New York<\/strong>).<\/li>\n<\/ol>\n

        You can apply a single time zone to the entire dataset, or ask Forecast to derive a time zone from the geolocation of each item ID in the target time series dataset.<\/p>\n

        \"You<\/p>\n

          \n
        1. In the navigation pane, under your dataset, choose Predictors<\/strong>.<\/li>\n
        2. Choose Train<\/strong> predictor<\/strong>.<\/li>\n<\/ol>\n

          \"You<\/p>\n

            \n
          1. For Forecast horizon<\/strong>, choose 168<\/strong>.<\/li>\n
          2. For Forecast frequency<\/strong>, choose hour<\/strong>.<\/li>\n
          3. For Number of backtest windows<\/strong>, choose 3<\/strong>.<\/li>\n
          4. For Backtest window offset<\/strong>, choose 168<\/strong>.<\/li>\n
          5. For Forecast types<\/strong>, choose p50, p60, and p70.<\/li>\n
          6. For Algorithm<\/strong>, you can either select AutoML for Forecast to find the best algorithm for your dataset or select a specific algorithm. For this post, we select DeepAR+ <\/strong>with Hyperparameter optimization<\/strong> turned on for Forecast to optimize the model.<\/li>\n
          7. Under Built-in datasets<\/strong>, select Enable Weather Index<\/strong> to apply the Weather Index to your training model. For this post, we have also selected Enable Holidays for US<\/strong>, as we hypothesize that holidays will have an impact on the demand for Taxis.<\/li>\n<\/ol>\n

            If you\u2019re following the notebook in our GitHub repo<\/a>, we call this predictor nyctaxi_demo_weather_deepar<\/code>. While training the model, Forecast uses the historical weather to apply the Weather Index to only those items that are impacted by weather to improve item level accuracy.<\/p>\n

            \"Forecast<\/p>\n

              \n
            1. After your predictor is trained, choose your predictor on the Predictors<\/strong> page to view the details of the accuracy metrics.<\/li>\n<\/ol>\n

              \"16.\u00a0<\/em><\/p>\n

                \n
              1. On the predictor\u2019s details page, you can review the model accuracy numbers and choose Export backtest results<\/strong> in the Predictor metrics<\/strong>\n<\/li>\n<\/ol>\n

                Forecast provides different model accuracy metrics for you to assess the strength of your forecasting models. We provide the weighted quantile loss (wQL) metric for each selected distribution point, also called quantiles<\/em>, and weighted absolute percentage error (WAPE) and root mean square error (RMSE), calculated at the mean forecast. For each metric, a lower value indicates a smaller error and therefore a more accurate model. All these accuracy metrics are non-negative. Quantiles are specified when choosing your forecast type. For more information about how each metric is calculated and recommendations for the best use case for each metric, see Measuring forecast model accuracy to optimize your business objectives with Amazon Forecast<\/a>.<\/p>\n

                \"Quantiles<\/p>\n

                  \n
                1. For S3 predictor backtest export location<\/strong>, enter the details of your Amazon Simple Storage Service<\/a> (Amazon S3) location for exporting the CSV files.<\/li>\n<\/ol>\n

                  \"For<\/p>\n

                  Exporting the backtest results downloads the forecasts from the backtesting for each item and the accuracy metrics for each item. This helps you measure the accuracy of forecasts for individual items, allowing you to better understand your forecasting model\u2019s performance for the items that most impact your business. For more information about the benefits of exporting backtest results, see Amazon Forecast now supports accuracy measurements for individual items<\/a>.<\/p>\n

                  In the next section of this post, we use these backtest results to assess the accuracy improvements of enabling the Weather Index by comparing the accuracy of specific items between models where you have not enabled the Weather Index.<\/p>\n

                    \n
                  1. After you evaluate the model accuracy, you can start creating forecasts by choosing Forecasts <\/strong>in the navigation pane.<\/li>\n
                  2. Choose Create a forecast<\/strong>.<\/li>\n<\/ol>\n

                    \"Choose<\/p>\n

                    To create these forecasts, Forecast automatically pulls in the weather forecasts for the next 14 days and applies the weather prediction to only those item IDs that are influenced by weather. In our example, we create forecasts for the next 7 days with hourly frequency.<\/p>\n

                    Assessing the impact of the Weather Index<\/h2>\n

                    To assess the impact of adding weather information to your forecasting models, we can create another predictor with the same dataset and settings, but this time without enabling the Weather Index. If you\u2019re following the notebook in our GitHub repo<\/a>, we call this predictor nyctaxi_demo_baseline_deepar<\/code>.<\/p>\n

                    When creating this predictor, you should not select Hyperparameter optimization<\/strong> for DeepAR+, but rather use the winning training parameters from the hyperparameter optimization of DeepAR+ model of nyctaxi_demo_weather_deepar<\/code> as the training parameters setting, for a fair comparison between the two models. You can find the winning training parameters in the predictor details page under the Predictor metrics<\/strong> section. For this post, these are as follows.<\/p>\n

                    \n
                    \"context_length\": \"63\",\r\n\"epochs\": \"500\",\r\n\"learning_rate\": \"0.014138165570842774\",\r\n\"learning_rate_decay\": \"0.5\",\r\n\"likelihood\": \"student-t\",\r\n\"max_learning_rate_decays\": \"0\",\r\n\"num_averaged_models\": \"1\",\r\n\"num_cells\": \"40\",\r\n\"num_layers\": \"2\",\r\n\"prediction_length\": \"168\"<\/code><\/pre>\n<\/div>\n
                    You can then go to the Predictors<\/strong> page to review the predictor metrics nyctaxi_demo_baseline_deepar<\/code>.<\/p>\n
                    The following screenshot shows the predictor details page for the nyctaxi_demo_baseline_deepar<\/code> model that is trained without enabling the Weather Index. The predictor metrics for nyctaxi_demo_weather_deepar<\/code> with weather enabled is shown above after the create predictor steps.<\/p>\n
                    \"The<\/p>\n
                    The following table summarizes the predictor metrics for the two models. Forecast provides the weighted quantile loss (wQL) metric for each quantile, and weighted absolute percentage error (WAPE) metric and root mean square error (RMSE) metric, calculated at the mean forecast. For each metric, a lower value indicates a smaller error and therefore a more accurate model. The model with the Weather Index is more accurate, with lower values for each metric.<\/p>\n\n\n\n\n\n
                    Predictor<\/strong><\/td>\nwQL[0.5]<\/strong><\/td>\nwQL[0.6]<\/strong><\/td>\nwQL[0.7]<\/strong><\/td>\nWAPE<\/strong><\/td>\nRMSE<\/strong><\/td>\n<\/tr>\n
                    nyctaxi_demo_baseline_deepar<\/strong><\/td>\n0.2637<\/td>\n0.2769<\/td>\n0.2679<\/td>\n0.2625<\/td>\n31.3986<\/td>\n<\/tr>\n
                    nyctaxi_demo_weather_deepar <\/strong><\/td>\n0.1646<\/td>\n0.1620<\/td>\n0.1498<\/td>\n0.1647<\/td>\n19.7874<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                    You can now export the backtest results for both predictors to assess the forecasting accuracy at an item level. With the backtest results, you can also use a visualization tool like Amazon QuickSight<\/a> to create graphs that help you visualize and compare the model accuracy of both the predictors by plotting the forecasts against actuals for items that are important for you. The following graph visualizes the comparison of the models with and without the Weather Index to the actual demand for a few items in the dataset at the 0.60 quantile.<\/p>\n
                    \"The<\/p>\n
                    For Feb 27, we have zoomed in to better assess the difference in accuracies at an hourly level.<\/p>\n
                    \"For<\/p>\n
                    Here we show the magnitude of error for each item id for the two models. Lower error values correspond to a more accurate model. Most items in the model with the Weather Index have errors below 0.05.<\/p>\n
                    \"Most<\/p>\n
                    Tips and best practices<\/h2>\n
                    When using the Weather Index, consider the following best practices:<\/p>\n