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Cleaning time series data requires multiple stages. First, identify and manage missing values by either imputing or deleting them. Then, look for and address outliers that might bias the analysis. Next, guarantee homogeneity in time intervals by interpolating or aggregating data as necessary. Normalise or scale the data to facilitate comparisons. Detrend the data to eliminate any long-term patterns. Additionally, address seasonality using approaches such as seasonal differencing or seasonal decomposition. Finally, try using smoothing techniques such as moving averages to decrease noise. Throughout the process, ensure data integrity and correctness. Effective time series data cleaning requires the use of proper tools and approaches that are suited to your individual dataset. for more information about free demo class online contact us today at Durga Online Trainer